R03 - DEA
Manon Martin
March 05, 2024
1 Notes
Differential Expression Analysis (DEA): proDA for DEA (no imputation, takes into account missing values)
Enrichment analysis (GSEA)
1.1 Current set-up
Lipid_Class_col <- c("brown", "brown1", "chocolate1", "orange", "gold",
"olivedrab1", "olivedrab", "limegreen","mediumseagreen",
"cadetblue1", "darkcyan", "blue","darkblue",
"darkviolet", "deeppink") Differential expression analysis (DEA) with all the cell lines.
- regression with
proDA, for all the cell lines - formula:
Treatment+Cell.Line+Cell.Line:Treatment
2 prostate
Reference cell line: 22RV1 Reference treatment:
Control
se$Treatment <- gsub(" |-","_",se$Treatment)
se$Treatment <- gsub("_+","_",se$Treatment)
rd <- rowData(se)
se$CL_Trt <- paste(se$Cell.Line, se$Treatment,sep = "_")
cd <- colData(se) %>% as.data.frame() %>% rownames_to_column("colname")
X <- assays(se)$log %>% as.data.frame() %>%
rownames_to_column()%>% pivot_longer(-rowname, names_to = "colname") %>%
left_join(cd, by = "colname")
df_NA <- X %>% group_by(Cell.Line, Treatment, rowname) %>%
summarize(sumNAs = sum(is.na(value)))## `summarise()` has grouped output by 'Cell.Line', 'Treatment'. You can override
## using the `.groups` argument.##
## 22RV1_ALA10 22RV1_Control 22RV1_Fer1_3 22RV1_PunA10
## 3 3 3 3
## 22RV1_PunA10_fer13 22RV1_PunA2.5 22RV1_PunA5 LNCaP_ALA10
## 3 3 3 3
## LNCaP_Control LNCaP_Fer1_3 LNCaP_PunA10 LNCaP_PunA10_fer13
## 3 3 3 3
## LNCaP_PunA2.5 LNCaP_PunA5 PC3_ALA10 PC3_Control
## 3 3 3 3
## PC3_Fer1_3 PC3_PunA10 PC3_PunA10_fer13 PC3_PunA2.5
## 3 3 3 3
## PC3_PunA5
## 3se$CL_Trt <- make.names(se$CL_Trt)
evalproDA <- FALSE
if (evalproDA){
# fit <- proDA(assays(se)$log, design = se$CL_Trt)
# save(fit, file = "results/R03_fitProDA_prostate.rda")
se_sub <- list()
for (i in unique(rd$Lipid_Class)){
se_sub[[i]] <- se[rd$Lipid_Class==i,]
}
fit_list <- lapply(se_sub, function(x)
proDA(assays(x)$log, design = x$CL_Trt))
save(fit_list, file = "results/R03_fitProDA_prostate.rda")
}else{
load(file = "results/R03_fitProDA_prostate.rda")
}
term2gene <- rowData(se) %>% as.data.frame() %>%
rownames_to_column("lipid") %>%
select(c("Lipid_Class","lipid"))
# for heatmaps
Treatment_col = c("Control" = "red", "ALA10" ="green" ,
"Fer1_3" = "goldenrod2",
"PunA2.5" = "darkturquoise", "PunA5" = "dodgerblue",
"PunA10" = "dodgerblue4", "PunA10_fer13" = "darkorchid3")fun_sign <- function(res){
res$sign <- 0
res$sign[res$diff>0 &res$adj_pval<0.05 ] = 1
res$sign[res$diff<0 &res$adj_pval<0.05 ] = -1
res$sign
}
merge_res_prostate <- function(append = FALSE,
pairs = FALSE){
compare <- paste(cdt1,"vs",cdt2)
pander(compare)
inter1 <- paste0("(LNCaP_",cdt1," - LNCaP_",cdt2,") - ",
"(X22RV1_",cdt1," - X22RV1_",cdt2,")")
inter2 <- paste0("(PC3_",cdt1," - PC3_",cdt2,") - ",
"(X22RV1_",cdt1," - X22RV1_",cdt2,")")
### Effects of treatment, per cell line
# main effect
name1 <- paste0("X22RV1_",cdt1," - X22RV1_",cdt2)
pander(name1)
# res_td1 <- test_diff(fit, name1, sort_by = NULL)
res_td1 <- lapply(fit_list, function(x) test_diff(x, name1, sort_by = NULL))
res_td1 <- do.call(rbind,res_td1)
res_td1$adj_pval <- p.adjust(res_td1$pval,method = "fdr")
res_td1$adj_pval[res_td1$adj_pval == 0] <-
min(res_td1$adj_pval[res_td1$adj_pval > 0])
res_td1$sign <- fun_sign(res_td1)
res_td1 %>% dplyr::select(name, pval, adj_pval, diff, t_statistic) %>%
arrange(pval) %>% filter(adj_pval<0.05) %>% pander()
# main effect
name2 <- paste0("LNCaP_",cdt1," - LNCaP_",cdt2)
pander(name2)
# res_td2 <- test_diff(fit, name2, sort_by = NULL)
res_td2 <- lapply(fit_list, function(x) test_diff(x, name2, sort_by = NULL))
res_td2 <- do.call(rbind,res_td2)
res_td2$adj_pval <- p.adjust(res_td2$pval,method = "fdr")
res_td2$adj_pval[res_td2$adj_pval == 0] <-
min(res_td2$adj_pval[res_td2$adj_pval > 0])
res_td2$sign <- fun_sign(res_td2)
res_td2 %>% dplyr::select(name, pval, adj_pval, diff, t_statistic) %>%
arrange(pval)%>% filter(adj_pval<0.05) %>% pander()
# main effect
name3 <- paste0("PC3_",cdt1," - PC3_",cdt2)
pander(name3)
# res_td3 <- test_diff(fit, name3, sort_by = NULL)
res_td3 <- lapply(fit_list, function(x) test_diff(x, name3, sort_by = NULL))
res_td3 <- do.call(rbind,res_td3)
res_td3$adj_pval <- p.adjust(res_td3$pval,method = "fdr")
res_td3$adj_pval[res_td3$adj_pval == 0] <-
min(res_td3$adj_pval[res_td3$adj_pval > 0])
res_td3$sign <- fun_sign(res_td3)
res_td3 %>% dplyr::select(name, pval, adj_pval, diff, t_statistic) %>%
arrange(pval)%>% filter(adj_pval<0.05) %>% pander()
# interaction terms
name_int1 <- paste0("int_LNCaP_",cdt1,"_",cdt2)
name_int2 <- paste0("int_PC3_",cdt1,"_",cdt2)
pander(name_int1)
# res_td_inter1 <- test_diff(fit, inter1,
# sort_by = NULL)
res_td_inter1 <- lapply(fit_list, function(x) test_diff(x, inter1, sort_by = NULL))
res_td_inter1 <- do.call(rbind,res_td_inter1)
res_td_inter1$adj_pval <- p.adjust(res_td_inter1$pval,method = "fdr")
res_td_inter1$sign <- fun_sign(res_td_inter1)
res_td_inter1 %>% dplyr::select(name, pval, adj_pval, diff, t_statistic) %>%
arrange(pval)%>% filter(adj_pval<0.05) %>% pander()
pander(name_int2)
# res_td_inter2 <- test_diff(fit, inter2,
# sort_by = NULL)
res_td_inter2 <- lapply(fit_list, function(x) test_diff(x, inter2, sort_by = NULL))
res_td_inter2 <- do.call(rbind,res_td_inter2)
res_td_inter2$adj_pval <- p.adjust(res_td_inter2$pval,method = "fdr")
res_td_inter2$sign <- fun_sign(res_td_inter2)
res_td_inter2 %>% dplyr::select(name, pval, adj_pval, diff, t_statistic) %>%
arrange(pval)%>% filter(adj_pval<0.05) %>% pander()
### all together
test <- c(rep(name1, nrow(res_td1)),
rep(name2, nrow(res_td2)),
rep(name3, nrow(res_td3)),
rep(name_int1, nrow(res_td_inter1)),
rep(name_int2, nrow(res_td_inter2)))
test <- factor(test, levels = c(name1, name2, name3, name_int1, name_int2))
#####
test2 <- c(name1,name2, name3, inter1, inter2)
df_NA_wide <- df_NA %>% ungroup() %>%
dplyr::select(!c(Cell.Line, Treatment)) %>%
pivot_wider(names_from = CL_trt, values_from = sumNAs)
t2 <- gsub("X22RV1","22RV1", test2)
tex <- strsplit(t2, split = "-")
tex <- lapply(tex, function(x) gsub(" |\\(|\\)","",x))
names(tex) <- c(name1, name2, name3, name_int1, name_int2)
matNumNA = matrix(NA, ncol=3, nrow=length(test))
numNA = c()
for (i in 1:length(tex)){
a = do.call(paste0, as.data.frame(df_NA_wide[,tex[[i]]]))
names(a) = df_NA_wide$rowname
numNA = c(numNA, a)
}
matNumNA = data.frame(name = names(numNA), numNA,
test = rep(names(tex),each = nrow(df_NA_wide)))
####
df_all <- data.frame(name = rep(res_td1$name,5), test,
lipid_Class = rep(rd$Lipid_Class,5),
logFC = c(res_td1$diff,res_td2$diff,res_td3$diff,
res_td_inter1$diff, res_td_inter2$diff),
t_statistic = c(res_td1$t_statistic,
res_td2$t_statistic, res_td3$t_statistic,
res_td_inter1$t_statistic, res_td_inter2$t_statistic),
adj_pval = c(res_td1$adj_pval,res_td2$adj_pval, res_td3$adj_pval,
res_td_inter1$adj_pval, res_td_inter2$adj_pval),
sign = c(res_td1$sign,res_td2$sign, res_td3$sign,
res_td_inter1$sign, res_td_inter2$sign))
df_all <- df_all %>% left_join(matNumNA, by = c("name", "test"))
df_all_wide <- df_all %>% pivot_wider(names_from = test,
values_from = c(logFC, adj_pval, numNA,
sign, t_statistic))
### volcano plots
p_VP <- df_all %>% ggplot(aes(x = logFC, y = -log10(adj_pval),
label = name)) +
geom_point(aes(color = lipid_Class)) +
facet_wrap(~test)+
theme_bw() + ggtitle(compare) +
scale_color_manual(values = Lipid_Class_col) +
geom_hline(yintercept = -log10(0.05),
linetype=2, color="blue")
# only lipids not totally missing in one condition
a = expand.grid(c(0,3),c(0,3), c(0,3),c(0,3))
val4 = do.call(paste0, as.data.frame(a))
val4 = val4[val4!="0000"]
val2 = c("30", "03", "33")
df_all_sub = df_all[!df_all$numNA %in% c(val2,val4),]
p_VP2 <- df_all_sub %>% ggplot(aes(x = logFC, y = -log10(adj_pval),
label = name)) +
geom_point(aes(color = lipid_Class)) +
facet_wrap(~test)+
theme_bw() + ggtitle(compare) +
scale_color_manual(values = Lipid_Class_col) +
geom_hline(yintercept = -log10(0.05),
linetype=2, color="blue")
### pairs plots
upperfun <- function(data,mapping){
ggplot(data = data, mapping = mapping)+
geom_point()+
geom_abline(intercept=0,slope=1, color="gray") +
scale_x_continuous(limits =range_val) +
scale_y_continuous(limits = range_val)
}
lowerfun <- function(data,mapping){
ggplot(data = data, mapping = mapping)+
geom_point()+
geom_abline(intercept=0,slope=1, color="gray") +
scale_x_continuous(limits = range_val) +
scale_y_continuous(limits = range_val)
}
p_logFC <- p_pvals <- NULL
if (pairs){
# foldchanges
range_val <- range(df_all_wide[,c(3:7)], na.rm = TRUE)
p_logFC <- ggpairs(df_all_wide[,c(3:7)],
upper = list(continuous = wrap(upperfun)),
lower = list(continuous = wrap(lowerfun)),
diag = list(continuous = "blankDiag"))
# p_logFC
# ggplotly(p_logFC)
# pvals
range_val <- range(-log10(df_all_wide[,c(8:12)]), na.rm = TRUE)
p_pvals <- ggpairs(-log10(df_all_wide[,c(8:12)]),
upper = list(continuous = wrap(upperfun)),
lower = list(continuous = wrap(lowerfun)),
diag = list(continuous = "blankDiag"))
# p_pvals
# ggplotly(p_pvals)
}
#### upsetplot
df <- data.frame(res_td1$sign,res_td2$sign,res_td3$sign,
res_td_inter1$sign, res_td_inter2$sign)
colnames(df) <- c(name1, name2, name3, name_int1, name_int2)
us_pos <- df %>% as.data.frame() %>% dplyr::mutate_all(~ifelse(.x==1,1,0))
us_neg <- df %>% as.data.frame() %>% dplyr::mutate_all(~ifelse(.x==-1,1,0))
list_ret <- list(df_all=df_all, df_all_wide=df_all_wide,
p_logFC = p_logFC, p_pvals = p_pvals, p_VP = p_VP,
p_VP2 = p_VP2,
us_pos = us_pos,us_neg=us_neg, effects = tex)
#### save results
write.xlsx(df_all_wide, file = "results/R03_prostate.xlsx",
sheetName = gsub(" ","",compare), append = append)
return(list_ret)
}
#### heatmaps
ht_fun <- function(){
X <- res$df_all_wide %>% select(c(name,starts_with("logFC_")))
Y <- res$df_all_wide %>% select(starts_with("adj_pval_"))
if (sum(Y<0.05)>1){
for (i in 2:ncol(X)){
X[Y[,(i-1)]>=0.05,i] = NA
}
# FC
X_fc = X[rowSums(is.na(X))!=(ncol(X)-1),-1] %>% as.matrix()
rownames(X_fc) <- X$name[rowSums(is.na(X))!=(ncol(X)-1)]
col1 = circlize::colorRamp2(seq(-max(X_fc, na.rm=TRUE),
max(X_fc, na.rm=TRUE), length = 3),
c("blue","white","red"))
names_sig <- X[rowSums(is.na(X))!=(ncol(X)-1),1] %>% pull()
ht1 <- Heatmap(X_fc, cluster_rows = FALSE, cluster_columns = FALSE,
column_title = "Fold changes", col=col1,
row_names_gp = gpar(fontsize = 8),
column_names_gp = gpar(fontsize = 8))
# raw values
X_raw <- assays(se)$raw
cols <- rev(heat.colors(20))
X_raw <- X_raw[names_sig,] %>% as.matrix()
cdtrt <- colData(se)[colnames(X_raw),"Treatment"]
row_ha = HeatmapAnnotation(Treatment = cdtrt,
col = list(Treatment = Treatment_col),
which = "column" )
ht2 <- Heatmap(X_raw, cluster_rows = FALSE, cluster_columns = FALSE,
column_title = "raw values", bottom_annotation = row_ha,
col=cols, row_names_gp = gpar(fontsize = 8),
column_names_gp = gpar(fontsize = 8))
# z scores
z_scores_fun <- function(data) {(data-mean(data, na.rm=TRUE))/sd(data, na.rm=TRUE)}
X_zscore <- t(apply(X_raw, 1,z_scores_fun))
col1 = circlize::colorRamp2(seq(-max(X_zscore, na.rm=TRUE),
max(X_zscore, na.rm=TRUE), length = 3),
c("blue","white","red"))
cdtrt <- colData(se)[colnames(X_raw),"Treatment"]
row_ha = HeatmapAnnotation(Treatment = cdtrt,
col = list(Treatment = Treatment_col),
which = "column" )
ht3 <- Heatmap(X_zscore, cluster_rows = FALSE, cluster_columns = FALSE,
column_title = "z-scores", bottom_annotation = row_ha, col=col1,
row_names_gp = gpar(fontsize = 8),
column_names_gp = gpar(fontsize = 8))
list(ht1,ht2,ht3)
}
}2.1 F test for the interaction effect
# load(file = paste0("processed_data/R02_se_",what_data,".rda"))
## -----------------------------------
# F test for the interaction effect
formFull <- ~ se$Treatment + se$Cell.Line + se$Treatment:se$Cell.Line
cat("formula: ", as.character(formFull))## formula: ~ se$Treatment + se$Cell.Line + se$Treatment:se$Cell.Linedesign <- model.matrix(formFull)
fitlogFull <- proDA(assays(se)$log, design = design,
data_is_log_transformed = TRUE,
verbose = FALSE)
fitlogFull$convergence$successful## [1] TRUEres <- test_diff(fitlogFull,
reduced_model = ~ se$Treatment + se$Cell.Line ,
alternative = "two.sided",
pval_adjust_method = "BH",
sort_by = "adj_pval",
verbose = F) %>%
as_tibble() %>% dplyr::rename("lipid" = "name",
"adj.P.Val" = "adj_pval")
res[res$adj.P.Val<0.05,]## # A tibble: 14 × 9
## lipid pval adj.P.Val f_statistic df1 df2 avg_abundance n_approx n_obs
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TG 54… 2.40e-9 6.51e-7 10.8 12 42.0 -3.04 59.0 59
## 2 TG 52… 5.08e-9 6.88e-7 9.70 12 46.0 -3.42 63.0 63
## 3 TG 50… 6.44e-7 5.82e-5 7.12 12 43.0 -3.14 60.0 60
## 4 CE 18… 6.03e-6 4.08e-4 6.14 12 39.8 -3.05 56.9 57
## 5 TG 48… 2.03e-5 7.84e-4 5.76 12 36.0 -4.55 53.0 53
## 6 TG 56… 1.95e-5 7.84e-4 6.01 12 33.2 -4.78 50.3 50
## 7 CE 20… 1.73e-5 7.84e-4 5.45 12 42.2 -6.21 59.2 59
## 8 PE 34… 1.00e-4 3.02e-3 4.78 12 38.2 -3.09 55.3 55
## 9 CE 20… 9.93e-5 3.02e-3 4.48 12 46.0 -3.33 63.0 63
## 10 PE 36… 1.65e-4 4.47e-3 4.95 12 30.7 -3.19 47.8 48
## 11 TG 53… 2.93e-4 7.22e-3 4.04 12 45.0 -4.88 62.0 62
## 12 CE 20… 4.71e-4 1.06e-2 3.82 12 46.0 -3.15 63 63
## 13 PI 33… 1.72e-3 3.32e-2 3.50 12 36.2 -3.09 53.2 52
## 14 TG 52… 1.70e-3 3.32e-2 4.07 12 23.9 -3.99 40.9 412.2 PunA 10 vs CTL
2.2.1 DEA
all the lipids
only lipids not totally missing in ome condition
upregulated
if(sum(colSums(res$us_pos)>0)>1){
UpSetR::upset(as.data.frame(res$us_pos),
keep.order = TRUE, nsets = ncol(res$us_pos)) %>%
print()
}
downregulated
if(sum(colSums(res$us_neg)>0)>1){
UpSetR::upset(as.data.frame(res$us_neg),
keep.order = TRUE, nsets = ncol(res$us_neg)) %>%
print()
}
## [[1]]
##
## [[2]]
##
## [[3]]
2.2.2 GSEA
GSEA
df = res$df_all_wide
ranks <- df[,grep("t_statistic_",colnames(df), value=TRUE)]
colnames(ranks) <- sub("t_statistic_","",colnames(ranks))
ll = list()
for (i in unique(term2gene$Lipid_Class)){
ll[[i]] = term2gene$lipid[term2gene$Lipid_Class==i]
}
for (i in 1:ncol(ranks)){
print(colnames(ranks)[i])
rank1 <- ranks[,i] %>% pull()
names(rank1) = df$name
fgsea::fgsea(ll,rank1) %>% arrange(pval) %>% print()
}## [1] "X22RV1_PunA10 - X22RV1_Control"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: CL 0.08072917 0.4813360 0.25720647 -0.4653623 -1.4020718 27
## 2: Cer 0.08347530 0.4813360 0.20207171 -0.6602838 -1.3861417 7
## 3: TG 0.09694555 0.4813360 0.16318013 0.4413557 1.3493266 71
## 4: PG 0.17398946 0.4813360 0.13802224 -0.5801099 -1.2683993 8
## 5: PC 0.17868852 0.4813360 0.13077714 0.5122595 1.2836482 25
## 6: DG 0.19253438 0.4813360 0.13880511 0.5885236 1.2891316 13
## 7: PS 0.32549020 0.6974790 0.10244941 -0.4639513 -1.1146864 12
## 8: PE O 0.38461538 0.7211538 0.09923333 -0.3884007 -1.0645161 18
## 9: HexCer 0.46446701 0.7741117 0.09656296 0.6254682 1.0430481 4
## 10: CE 0.58602151 0.8206612 0.06553210 0.3937463 0.9247756 19
## 11: PC O 0.60181818 0.8206612 0.06494077 0.3858268 0.8934619 17
## 12: FC 0.83427495 0.9381818 0.05121843 0.5925926 0.7882653 1
## 13: SM 0.86630037 0.9381818 0.04821497 -0.3137452 -0.7058796 9
## 14: PE 0.89230769 0.9381818 0.04371258 0.2631135 0.6401948 23
## 15: PI 0.93818182 0.9381818 0.04424074 0.2519685 0.5834853 17
## leadingEdge
## <list>
## 1: CL 68:2,....
## 2: Cer 40:1....
## 3: TG 54:7,....
## 4: PG 34:1,....
## 5: PC 36:6,....
## 6: DG 36:3,....
## 7: PS 36:2,....
## 8: PE O-40:....
## 9: HexCer 3....
## 10: CE 18:3,....
## 11: PC O-32:....
## 12: FC
## 13: SM 34:1;....
## 14: PE 34:3,....
## 15: PI 33:0,....
## [1] "LNCaP_PunA10 - LNCaP_Control"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: TG 0.0005541142 0.008311713 0.47727082 0.6879875 1.4393377 71
## 2: PC 0.0184129352 0.138097014 0.35248786 0.7284835 1.4573577 25
## 3: HexCer 0.0799573007 0.338787732 0.28780513 -0.6853933 -1.4391750 4
## 4: PE O 0.0903433953 0.338787732 0.28780513 -0.3377010 -1.2839060 18
## 5: SM 0.3058823529 0.917647059 0.28201335 -0.3658068 -1.0856286 9
## 6: PS 0.3733601258 0.932203390 0.20350896 -0.3640940 -1.1949993 12
## 7: Cer 0.4350282486 0.932203390 0.05773085 0.6077567 1.0619380 7
## 8: CL 0.5236656596 0.950495050 0.04388798 0.4909318 0.9872661 27
## 9: PE 0.7247706422 0.950495050 0.02913955 0.4334019 0.8595013 23
## 10: DG 0.7544783983 0.950495050 0.02878615 0.4208639 0.7917813 13
## 11: CE 0.7625383828 0.950495050 0.02674567 0.4204959 0.8256954 19
## 12: FC 0.8605108055 0.950495050 0.05174055 -0.5703704 -0.7652324 1
## 13: PI 0.8689370485 0.950495050 0.01982611 0.3497906 0.6776936 17
## 14: PC O 0.9318885449 0.950495050 0.01502987 0.2965980 0.5746368 17
## 15: PG 0.9504950495 0.950495050 0.14040624 -0.2319392 -0.6665703 8
## leadingEdge
## <list>
## 1: TG 48:3,....
## 2: PC 34:3,....
## 3: HexCer 4....
## 4: PE O-40:....
## 5: SM 41:2;O2
## 6: PS 40:2,....
## 7: Cer 42:2....
## 8: CL 70:7,....
## 9: PE 36:3,....
## 10: DG 36:3,....
## 11: CE 18:3,....
## 12: FC
## 13: PI 38:6,....
## 14: PC O-40:....
## 15: PG 34:1,....
## [1] "PC3_PunA10 - PC3_Control"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: TG 0.01225094 0.1837642 0.38073040 0.5781669 1.4990280 71
## 2: DG 0.05723370 0.4292528 0.23779383 0.7344071 1.4530561 13
## 3: PG 0.15454545 0.7727273 0.16957064 -0.5930297 -1.2853637 8
## 4: SM 0.21709007 0.7866184 0.14205664 -0.5397227 -1.2156788 9
## 5: PC 0.29758713 0.7866184 0.08553569 0.5187745 1.1594152 25
## 6: PE O 0.32692308 0.7866184 0.13574094 -0.4283506 -1.1311337 18
## 7: PC O 0.36708861 0.7866184 0.12625399 -0.4167803 -1.0783680 17
## 8: PE 0.48373984 0.8238905 0.06130261 0.4534400 0.9982121 23
## 9: CE 0.49433428 0.8238905 0.06238615 0.4694242 1.0025276 19
## 10: Cer 0.57692308 0.8653846 0.07511816 -0.4492471 -0.9375195 7
## 11: CL 0.63745020 0.8692503 0.10473282 -0.2831632 -0.8535397 27
## 12: HexCer 0.73375262 0.9133214 0.06224904 0.4906367 0.7782818 4
## 13: PI 0.79154519 0.9133214 0.04190817 0.3551913 0.7398376 17
## 14: PS 0.89922481 0.9634551 0.06252374 -0.2745033 -0.6511333 12
## 15: FC 0.97804391 0.9780439 0.04660151 0.5111111 0.6867927 1
## leadingEdge
## <list>
## 1: TG 52:5,....
## 2: DG 36:3,....
## 3: PG 34:1,....
## 4: SM 41:2;....
## 5: PC 36:6,....
## 6: PE O-40:....
## 7: PC O-40:....
## 8: PE 36:6,....
## 9: CE 18:3,....
## 10: Cer 42:1....
## 11: CL 68:2,....
## 12: HexCer 4....
## 13: PI 33:0,....
## 14: PS 36:2,....
## 15: FC
## [1] "int_LNCaP_PunA10_Control"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: CL 0.0001611270 0.001811785 0.51884808 0.6228800 1.9085193 27
## 2: TG 0.0002415713 0.001811785 0.51884808 0.4932178 1.8098928 71
## 3: Cer 0.0137354533 0.068677267 0.38073040 0.7366974 1.6310086 7
## 4: DG 0.0219779633 0.082417362 0.35248786 -0.5348837 -1.6059893 13
## 5: HexCer 0.1277372263 0.383211679 0.24133998 -0.7116105 -1.3871844 4
## 6: PC 0.3522167488 0.880541872 0.07217980 0.3690642 1.1128311 25
## 7: PE 0.4351050680 0.932368003 0.06197627 0.3532471 1.0468890 23
## 8: PE O 0.6611675127 0.956015524 0.04380020 0.3193000 0.8943665 18
## 9: PG 0.6851119895 0.956015524 0.04388798 0.3776389 0.8611014 8
## 10: FC 0.7682926829 0.956015524 0.05859376 -0.6111111 -0.8106769 1
## 11: CE 0.7716894977 0.956015524 0.10135074 -0.2401589 -0.8266464 19
## 12: SM 0.9004149378 0.956015524 0.08679498 -0.2366412 -0.6240754 9
## 13: PS 0.9522580645 0.956015524 0.02723722 0.2371111 0.6031932 12
## 14: PI 0.9534282018 0.956015524 0.02730747 0.2171634 0.5969215 17
## 15: PC O 0.9560155239 0.956015524 0.02716698 0.2143254 0.5891209 17
## leadingEdge
## <list>
## 1: CL 70:7,....
## 2: TG 52:0,....
## 3: Cer 42:2....
## 4: DG 36:3,....
## 5: HexCer 4....
## 6: PC 30:0,....
## 7: PE 36:6,....
## 8: PE O-38:....
## 9: PG 34:0,....
## 10: FC
## 11: CE 18:3
## 12: SM 41:2;....
## 13: PS 36:2,....
## 14: PI 34:2,....
## 15: PC O-40:....
## [1] "int_PC3_PunA10_Control"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: PE 0.1379310 0.7853810 0.14733121 0.5647417 1.2767747 23
## 2: TG 0.1386963 0.7853810 0.13725078 0.4511284 1.2347137 71
## 3: CL 0.1570762 0.7853810 0.13649044 0.5326275 1.2509181 27
## 4: SM 0.2314815 0.8680556 0.13725078 -0.5918379 -1.2000842 9
## 5: Cer 0.3508772 0.9937500 0.09139243 0.6264035 1.1168910 7
## 6: HexCer 0.4882075 0.9937500 0.08943668 -0.6183063 -1.0261063 4
## 7: PS 0.5000000 0.9937500 0.08862611 -0.4489500 -0.9809758 12
## 8: PI 0.5714286 0.9937500 0.06364241 0.4376002 0.9355529 17
## 9: FC 0.8394584 0.9937500 0.05216303 -0.5777778 -0.7641542 1
## 10: PC O 0.9164619 0.9937500 0.05922192 -0.2834646 -0.6727696 17
## 11: DG 0.9212411 0.9937500 0.05760911 -0.2954497 -0.6566128 13
## 12: PE O 0.9386189 0.9937500 0.05998925 -0.2679137 -0.6413604 18
## 13: CE 0.9560976 0.9937500 0.03824168 0.2720070 0.6009250 19
## 14: PG 0.9930435 0.9937500 0.03959791 0.2361300 0.4281575 8
## 15: PC 0.9937500 0.9937500 0.03456643 0.2202030 0.5061634 25
## leadingEdge
## <list>
## 1: PE 36:6,....
## 2: TG 53:1,....
## 3: CL 72:3,....
## 4: SM 41:1;....
## 5: Cer 40:1....
## 6: HexCer 4....
## 7: PS 38:0,....
## 8: PI 33:0,....
## 9: FC
## 10: PC O-40:....
## 11: DG 36:3,....
## 12: PE O-32:....
## 13: CE 22:3,....
## 14: PG 36:0,....
## 15: PC 36:4,....2.3 ALA 10 vs CTL
2.3.1 DEA
all the lipids
only lipids not totally missing in ome condition
upregulated
if(sum(colSums(res$us_pos)>0)>1){
UpSetR::upset(as.data.frame(res$us_pos),
keep.order = TRUE, nsets = ncol(res$us_pos)) %>%
print()
}
downregulated
if(sum(colSums(res$us_neg)>0)>1){
UpSetR::upset(as.data.frame(res$us_neg),
keep.order = TRUE, nsets = ncol(res$us_neg)) %>%
print()
}
## [[1]]
##
## [[2]]
##
## [[3]]
2.3.2 GSEA
GSEA
df = res$df_all_wide
ranks <- df[,grep("t_statistic_",colnames(df), value=TRUE)]
colnames(ranks) <- sub("t_statistic_","",colnames(ranks))
ll = list()
for (i in unique(term2gene$Lipid_Class)){
ll[[i]] = term2gene$lipid[term2gene$Lipid_Class==i]
}
for (i in 1:ncol(ranks)){
print(colnames(ranks)[i])
rank1 <- ranks[,i] %>% pull()
names(rank1) = df$name
fgsea::fgsea(ll,rank1) %>% arrange(pval) %>% print()
}## [1] "X22RV1_ALA10 - X22RV1_Control"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: HexCer 0.03784055 0.2967258 0.32177592 -0.8172084 -1.4796251 4
## 2: TG 0.03956344 0.2967258 0.26635066 0.4459347 1.4611222 71
## 3: PE O 0.09375000 0.4665261 0.21140019 -0.4972175 -1.4128242 18
## 4: CL 0.18072289 0.4665261 0.16080140 -0.3871451 -1.2297853 27
## 5: PG 0.18181818 0.4665261 0.13880511 -0.5666510 -1.2571702 8
## 6: CE 0.21388368 0.4665261 0.12750532 0.4932461 1.2392472 19
## 7: SM 0.21771218 0.4665261 0.12503337 -0.5303008 -1.2321490 9
## 8: PC 0.39414802 0.7390275 0.08383611 0.3969666 1.0554243 25
## 9: PE 0.45901639 0.7650273 0.09255289 -0.3250061 -0.9856714 23
## 10: Cer 0.55818182 0.7670330 0.06863256 -0.4416569 -0.9375852 7
## 11: PI 0.60761905 0.7670330 0.06674261 0.3622047 0.8875978 17
## 12: DG 0.63253012 0.7670330 0.06736239 0.3847688 0.8702553 13
## 13: PC O 0.66476190 0.7670330 0.06238615 0.3464567 0.8490066 17
## 14: PS 0.92040816 0.9861516 0.05039643 0.2548263 0.5645559 12
## 15: FC 0.99038462 0.9903846 0.04432935 0.5074074 0.6834790 1
## leadingEdge
## <list>
## 1: HexCer 4....
## 2: TG 54:7,....
## 3: PE O-36:....
## 4: CL 68:2,....
## 5: PG 34:1,....
## 6: CE 18:3,....
## 7: SM 41:1;....
## 8: PC 36:6,....
## 9: PE 36:6,....
## 10: Cer 40:1....
## 11: PI 33:0,....
## 12: DG 36:3,....
## 13: PC O-32:....
## 14: PS 40:2,....
## 15: FC
## [1] "LNCaP_ALA10 - LNCaP_Control"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: TG 1.209073e-05 0.0001813609 0.59332548 0.7561822 1.5433865 71
## 2: PE 3.791711e-02 0.1916853794 0.32177592 -0.5190117 -1.5728078 23
## 3: HexCer 3.833708e-02 0.1916853794 0.32177592 -0.8726592 -1.6722974 4
## 4: CL 1.235602e-01 0.4633507853 0.12503337 0.6465554 1.2548213 27
## 5: PC 1.888772e-01 0.5666316894 0.09754492 0.6278505 1.2069459 25
## 6: PI 3.250000e-01 0.8125000000 0.28201335 -0.4338350 -1.1121722 17
## 7: CE 4.628633e-01 0.9382022472 0.05258990 0.5575575 1.0391414 19
## 8: PE O 6.319218e-01 0.9382022472 0.03871667 0.5095084 0.9398955 18
## 9: DG 7.303867e-01 0.9382022472 0.03272419 0.4730383 0.8486963 13
## 10: PS 8.272727e-01 0.9382022472 0.14463053 -0.2949160 -0.6919918 12
## 11: FC 8.336520e-01 0.9382022472 0.05195125 -0.5777778 -0.7811448 1
## 12: PC O 8.904555e-01 0.9382022472 0.02136303 0.3648513 0.6695015 17
## 13: Cer 9.038687e-01 0.9382022472 0.02491974 0.3705749 0.6263223 7
## 14: PG 9.195402e-01 0.9382022472 0.02286940 0.3517165 0.6059644 8
## 15: SM 9.382022e-01 0.9382022472 0.02034262 0.3258831 0.5686783 9
## leadingEdge
## <list>
## 1: TG 48:3,....
## 2: PE 36:6,....
## 3: HexCer 4....
## 4: CL 70:7,....
## 5: PC 34:3,....
## 6: PI 33:0
## 7: CE 18:3,....
## 8: PE O-36:....
## 9: DG 36:4,....
## 10: PS 36:1,....
## 11: FC
## 12: PC O-40:....
## 13: Cer 42:2....
## 14: PG 34:0,....
## 15: SM 36:1;....
## [1] "PC3_ALA10 - PC3_Control"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: TG 0.02757014 0.4135521 0.35248786 0.4761753 1.5281495 71
## 2: CE 0.14853195 0.7141794 0.14920754 0.5276939 1.3237847 19
## 3: CL 0.15143603 0.7141794 0.18470647 -0.4081493 -1.2690231 27
## 4: SM 0.20309478 0.7141794 0.13355495 -0.5513874 -1.2115231 9
## 5: PE O 0.30046948 0.7141794 0.11934844 -0.4164718 -1.1266014 18
## 6: Cer 0.39097744 0.7141794 0.08916471 -0.5225412 -1.0682127 7
## 7: DG 0.52016985 0.7141794 0.08020234 -0.3973645 -0.9774382 13
## 8: PC 0.52528548 0.7141794 0.06628422 0.3568002 0.9535006 25
## 9: PS 0.54372624 0.7141794 0.07217980 0.4169884 0.9356761 12
## 10: PE 0.57430730 0.7141794 0.08407456 -0.3155340 -0.9245744 23
## 11: PC O 0.58035714 0.7141794 0.07707367 -0.3498439 -0.9343200 17
## 12: HexCer 0.61250000 0.7141794 0.08020234 0.5393258 0.8867921 4
## 13: PG 0.61895551 0.7141794 0.06658921 -0.4360203 -0.9160318 8
## 14: PI 0.73214286 0.7844388 0.06538342 -0.3098424 -0.8274889 17
## 15: FC 0.98058252 0.9805825 0.04522474 -0.5074074 -0.6871725 1
## leadingEdge
## <list>
## 1: TG 52:5,....
## 2: CE 18:3,....
## 3: CL 68:2,....
## 4: SM 41:2;....
## 5: PE O-40:....
## 6: Cer 42:1....
## 7: DG 36:1,....
## 8: PC 36:6,....
## 9: PS 38:2,....
## 10: PE 36:2,....
## 11: PC O-34:....
## 12: HexCer 4....
## 13: PG 34:1,....
## 14: PI 36:1,....
## 15: FC
## [1] "int_LNCaP_ALA10_Control"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: CL 2.289616e-05 0.0003434424 0.57561026 0.6612638 2.0293076 27
## 2: TG 3.537415e-02 0.2444444444 0.28201335 0.3751019 1.4132082 71
## 3: HexCer 4.888889e-02 0.2444444444 0.24891111 0.7829129 1.4278315 4
## 4: PE O 9.275362e-02 0.3478260870 0.17520405 0.4910292 1.3477904 18
## 5: CE 1.242038e-01 0.3726114650 0.22798720 -0.4370971 -1.3131180 19
## 6: SM 2.765648e-01 0.5607476636 0.09435636 0.5045048 1.1461491 9
## 7: PC 3.304965e-01 0.5607476636 0.08289621 0.3671294 1.1068373 25
## 8: DG 3.333333e-01 0.5607476636 0.13010563 -0.4069767 -1.0752159 13
## 9: PG 3.728571e-01 0.5607476636 0.07687367 0.4883977 1.0862729 8
## 10: PI 3.738318e-01 0.5607476636 0.12384217 -0.3735868 -1.0702299 17
## 11: PS 4.531722e-01 0.6179620983 0.10882013 -0.3899614 -1.0049053 12
## 12: Cer 5.451895e-01 0.6814868805 0.05909548 0.4438176 0.9451419 7
## 13: PC O 7.312775e-01 0.8437817689 0.04586203 0.3042717 0.8246193 17
## 14: PE 8.717105e-01 0.8949494949 0.07608372 -0.2328846 -0.7369736 23
## 15: FC 8.949495e-01 0.8949494949 0.05121843 -0.5518519 -0.7430596 1
## leadingEdge
## <list>
## 1: CL 68:2,....
## 2: TG 52:0,....
## 3: HexCer 4....
## 4: PE O-40:....
## 5: CE 18:3,....
## 6: SM 41:1;....
## 7: PC 30:0,....
## 8: DG 36:3,....
## 9: PG 34:0,....
## 10: PI 33:0
## 11: PS 40:2,....
## 12: Cer 42:2....
## 13: PC O-40:....
## 14: PE 34:3
## 15: FC
## [1] "int_PC3_ALA10_Control"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: DG 0.1000000 0.9964974 0.18820415 -0.6118086 -1.3701802 13
## 2: TG 0.1811723 0.9964974 0.13574094 -0.3638844 -1.1621467 71
## 3: PI 0.2381786 0.9964974 0.11524000 -0.4979655 -1.1897834 17
## 4: PE 0.3758701 0.9964974 0.10395847 0.4050442 1.0542475 23
## 5: PE O 0.4269142 0.9964974 0.09624060 0.4256884 1.0496053 18
## 6: CE 0.4507042 0.9964974 0.09374654 0.4135356 1.0214419 19
## 7: HexCer 0.4767184 0.9964974 0.08730983 0.5988993 1.0300150 4
## 8: CL 0.5594714 0.9964974 0.07829552 0.3401639 0.9355716 27
## 9: PS 0.7178899 0.9964974 0.06767604 0.3575410 0.8012845 12
## 10: Cer 0.7766143 0.9964974 0.05101141 -0.4063101 -0.7786956 7
## 11: PG 0.9004630 0.9964974 0.05724611 0.2965779 0.6038404 8
## 12: PC 0.9019608 0.9964974 0.04513442 -0.2667336 -0.7028890 25
## 13: FC 0.9403579 0.9964974 0.04821497 0.5296296 0.7148932 1
## 14: SM 0.9772727 0.9964974 0.05258990 0.2233870 0.4678191 9
## 15: PC O 0.9964974 0.9964974 0.03975965 -0.1850394 -0.4421125 17
## leadingEdge
## <list>
## 1: DG 36:3,....
## 2: TG 50:4,....
## 3: PI 40:4,....
## 4: PE 36:6,....
## 5: PE O-36:....
## 6: CE 20:5,....
## 7: HexCer 4....
## 8: CL 68:2,....
## 9: PS 38:2,....
## 10: Cer 42:1....
## 11: PG 36:0,....
## 12: PC 34:3,....
## 13: FC
## 14: SM 41:1;....
## 15: PC O-32:....2.4 ALA 10 vs PunA 20
2.4.1 DEA
all the lipids
only lipids not totally missing in ome condition
upregulated
if(sum(colSums(res$us_pos)>0)>1){
UpSetR::upset(as.data.frame(res$us_pos),
keep.order = TRUE, nsets = ncol(res$us_pos)) %>%
print()
}## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
downregulated
if(sum(colSums(res$us_neg)>0)>1){
UpSetR::upset(as.data.frame(res$us_neg),
keep.order = TRUE, nsets = ncol(res$us_neg)) %>%
print()
}
## [[1]]
##
## [[2]]
##
## [[3]]
2.4.2 GSEA
GSEA
df = res$df_all_wide
ranks <- df[,grep("t_statistic_",colnames(df), value=TRUE)]
colnames(ranks) <- sub("t_statistic_","",colnames(ranks))
ll = list()
for (i in unique(term2gene$Lipid_Class)){
ll[[i]] = term2gene$lipid[term2gene$Lipid_Class==i]
}
for (i in 1:ncol(ranks)){
print(colnames(ranks)[i])
rank1 <- ranks[,i] %>% pull()
names(rank1) = df$name
fgsea::fgsea(ll,rank1) %>% arrange(pval) %>% print()
}## [1] "X22RV1_ALA10 - X22RV1_PunA10"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: CE 0.007092648 0.1063897 0.40701792 0.7186537 1.6984545 19
## 2: TG 0.083146067 0.4220532 0.23439265 0.4220445 1.2941030 71
## 3: HexCer 0.101886792 0.4220532 0.19189224 -0.8338074 -1.3542613 4
## 4: SM 0.127490040 0.4220532 0.17520405 -0.7018814 -1.3646282 9
## 5: PE O 0.140684411 0.4220532 0.16197895 -0.5807129 -1.3273466 18
## 6: PC 0.176583493 0.4414587 0.14375899 -0.5175922 -1.2605159 25
## 7: PE 0.210116732 0.4502501 0.13145761 -0.5153144 -1.2338852 23
## 8: Cer 0.298210736 0.5591451 0.10882013 0.6184805 1.1773579 7
## 9: DG 0.365656566 0.6094276 0.09721508 -0.5303192 -1.1105642 13
## 10: PI 0.439834025 0.6597510 0.08809450 0.4489090 1.0306944 17
## 11: FC 0.629558541 0.7987452 0.06538342 -0.6888889 -0.9153803 1
## 12: CL 0.638996139 0.7987452 0.06494077 -0.3600133 -0.8891173 27
## 13: PS 0.692622951 0.7991803 0.06392719 0.3887297 0.8408211 12
## 14: PG 0.844488189 0.9048088 0.05269731 0.3560851 0.7000639 8
## 15: PC O 1.000000000 1.0000000 0.04735342 0.2007874 0.4610076 17
## leadingEdge
## <list>
## 1: CE 20:3,....
## 2: TG 55:5,....
## 3: HexCer 4....
## 4: SM 41:1;....
## 5: PE O-32:....
## 6: PC 38:5,....
## 7: PE 36:6,....
## 8: Cer 40:1....
## 9: DG 34:4,....
## 10: PI 36:3,....
## 11: FC
## 12: CL 68:2,....
## 13: PS 36:2,....
## 14: PG 36:2,....
## 15: PC O-32:....
## [1] "LNCaP_ALA10 - LNCaP_PunA10"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: PE 0.004310851 0.06466276 0.40701792 -0.7477054 -1.6894088 23
## 2: TG 0.067484663 0.42468856 0.17232434 -0.5351714 -1.3567163 71
## 3: CL 0.108627943 0.42468856 0.28780513 0.3696614 1.3387663 27
## 4: PC 0.113250283 0.42468856 0.13725078 -0.6120318 -1.3952369 25
## 5: PG 0.154440154 0.43674699 0.22496609 0.5392134 1.3606855 8
## 6: PC O 0.174698795 0.43674699 0.26635066 0.4151186 1.2983419 17
## 7: PE O 0.204968944 0.43921917 0.24891111 0.3898423 1.2543150 18
## 8: SM 0.259259259 0.48611111 0.17669427 0.4540742 1.1774040 9
## 9: Cer 0.332413793 0.55402299 0.08108021 -0.6130055 -1.1373036 7
## 10: PI 0.376794258 0.56519139 0.06736239 -0.5071274 -1.0936596 17
## 11: DG 0.518796992 0.70745044 0.05434344 -0.4630633 -0.9526787 13
## 12: CE 0.684023669 0.82417582 0.03903553 -0.3784472 -0.8285760 19
## 13: HexCer 0.714285714 0.82417582 0.04754342 -0.4890593 -0.8180769 4
## 14: PS 0.771028037 0.82610147 0.10282184 0.2741619 0.7668683 12
## 15: FC 0.989384289 0.98938429 0.04889708 -0.5074074 -0.6683274 1
## leadingEdge
## <list>
## 1: PE 36:6,....
## 2: TG 48:3,....
## 3: CL 72:11....
## 4: PC 36:6,....
## 5: PG 36:0,....
## 6: PC O-34:....
## 7: PE O-40:....
## 8: SM 41:2;O2
## 9: Cer 34:1....
## 10: PI 33:0,....
## 11: DG 34:2,....
## 12: CE 22:3,....
## 13: HexCer 4....
## 14: PS 40:2,....
## 15: FC
## [1] "PC3_ALA10 - PC3_PunA10"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: DG 0.003940506 0.05910759 0.40701792 -0.7365746 -1.6473429 13
## 2: CE 0.016503448 0.12377586 0.35248786 0.6291235 1.6600346 19
## 3: PS 0.039848675 0.19924337 0.32177592 0.6689619 1.5741585 12
## 4: PE 0.077265973 0.28974740 0.19578900 -0.5374057 -1.3551859 23
## 5: TG 0.150000000 0.45000000 0.13145761 -0.3845716 -1.2166011 71
## 6: CL 0.218658892 0.54664723 0.10882013 -0.4568324 -1.1949701 27
## 7: Cer 0.373887240 0.70675105 0.07871138 -0.5743381 -1.0972940 7
## 8: PI 0.398791541 0.70675105 0.07627972 -0.4554144 -1.0681786 17
## 9: HexCer 0.424050633 0.70675105 0.11623415 0.5842697 1.0314624 4
## 10: SM 0.483918129 0.71316614 0.06494077 -0.4961832 -1.0066006 9
## 11: PC 0.542097489 0.71316614 0.05998925 -0.3710754 -0.9482267 25
## 12: PG 0.570532915 0.71316614 0.09688777 0.4258555 0.9117146 8
## 13: PC O 0.626470588 0.72285068 0.08783126 0.3425197 0.8914634 17
## 14: PE O 0.794336811 0.85107515 0.04275914 -0.3291112 -0.7881331 18
## 15: FC 0.990530303 0.99053030 0.04362512 -0.5074074 -0.6763182 1
## leadingEdge
## <list>
## 1: DG 34:1,....
## 2: CE 20:5,....
## 3: PS 40:5,....
## 4: PE 36:6,....
## 5: TG 53:1,....
## 6: CL 72:3,....
## 7: Cer 42:2....
## 8: PI 36:3,....
## 9: HexCer 4....
## 10: SM 36:1;....
## 11: PC 40:4,....
## 12: PG 32:0,....
## 13: PC O-36:....
## 14: PE O-36:....
## 15: FC
## [1] "int_LNCaP_ALA10_PunA10"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: PE 0.0004624682 0.006370587 0.49849311 -0.7559460 -1.7530125 23
## 2: CE 0.0008831596 0.006370587 0.47727082 -0.7647952 -1.7193144 19
## 3: SM 0.0012741175 0.006370587 0.45505987 0.8474288 1.9397213 9
## 4: PE O 0.0022085038 0.008281889 0.43170770 0.6890855 1.9199218 18
## 5: TG 0.0154904370 0.046471311 0.38073040 -0.5285027 -1.4350582 71
## 6: CL 0.0709784856 0.177446214 0.28780513 0.4511914 1.3731665 27
## 7: Cer 0.1354642314 0.290280496 0.14641624 -0.7116979 -1.3069847 7
## 8: HexCer 0.1897810219 0.333775420 0.15740290 0.7541809 1.3374509 4
## 9: PI 0.2002652520 0.333775420 0.10839426 -0.5598265 -1.2421211 17
## 10: PC O 0.4112903226 0.616935484 0.13574094 0.3685978 1.0113233 17
## 11: PC 0.6086419753 0.829966330 0.04632278 -0.3879602 -0.9145102 25
## 12: FC 0.8603773585 0.928359474 0.04989074 0.5777778 0.7593930 1
## 13: DG 0.8645833333 0.928359474 0.07934350 0.2731854 0.6981434 13
## 14: PG 0.8664688427 0.928359474 0.07113274 0.3079848 0.6694993 8
## 15: PS 0.9675324675 0.967532468 0.07011322 0.2383135 0.6027458 12
## leadingEdge
## <list>
## 1: PE 36:6,....
## 2: CE 20:3,....
## 3: SM 41:1;....
## 4: PE O-40:....
## 5: TG 60:5,....
## 6: CL 72:11....
## 7: Cer 40:1....
## 8: HexCer 4....
## 9: PI 33:0,....
## 10: PC O-34:....
## 11: PC 36:6,....
## 12: FC
## 13: DG 34:4,....
## 14: PG 36:0,....
## 15: PS 32:1,....
## [1] "int_PC3_ALA10_PunA10"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: TG 0.06258322 0.3504673 0.20658792 -0.4061365 -1.3475603 71
## 2: PI 0.06666667 0.3504673 0.21392786 -0.5774767 -1.4574850 17
## 3: DG 0.07009346 0.3504673 0.21140019 -0.6050041 -1.4255070 13
## 4: Cer 0.14262821 0.4111842 0.14641624 -0.6437907 -1.2978549 7
## 5: PE O 0.19252874 0.4111842 0.17093234 0.4283403 1.2404534 18
## 6: HexCer 0.20053476 0.4111842 0.16080140 0.7090467 1.3107286 4
## 7: PS 0.21204188 0.4111842 0.15419097 0.4683775 1.2340773 12
## 8: CE 0.21929825 0.4111842 0.16080140 0.4097502 1.1993301 19
## 9: CL 0.26883309 0.4480551 0.09688777 -0.4155181 -1.1507345 27
## 10: PE 0.34077381 0.5111607 0.08383611 -0.4064870 -1.0939416 23
## 11: SM 0.41018767 0.5593468 0.10755438 0.4349332 1.0276703 9
## 12: PG 0.83739837 0.9548715 0.06831109 0.3117871 0.6973891 8
## 13: PC 0.87654321 0.9548715 0.07253519 0.2360181 0.7329615 25
## 14: FC 0.89121339 0.9548715 0.05302125 0.5518519 0.7514273 1
## 15: PC O 0.98538012 0.9853801 0.06407038 0.1811024 0.5142584 17
## leadingEdge
## <list>
## 1: TG 53:1,....
## 2: PI 36:3,....
## 3: DG 38:4,....
## 4: Cer 42:2....
## 5: PE O-36:....
## 6: HexCer 4....
## 7: PS 40:5,....
## 8: CE 20:5,....
## 9: CL 72:3,....
## 10: PE 36:4,....
## 11: SM 41:1;....
## 12: PG 34:0,....
## 13: PC 38:5,....
## 14: FC
## 15: PC O-36:....2.5 PunA 2.5 vs PunA 5
2.5.1 DEA
all the lipids
only lipids not totally missing in ome condition
upregulated
if(sum(colSums(res$us_pos)>0)>1){
UpSetR::upset(as.data.frame(res$us_pos),
keep.order = TRUE, nsets = ncol(res$us_pos)) %>%
print()
}
downregulated
if(sum(colSums(res$us_neg)>0)>1){
UpSetR::upset(as.data.frame(res$us_neg),
keep.order = TRUE, nsets = ncol(res$us_neg)) %>%
print()
}
#### heatmaps
ht_fun()## [[1]]
##
## [[2]]
##
## [[3]]
2.5.2 GSEA
GSEA
df = res$df_all_wide
ranks <- df[,grep("t_statistic_",colnames(df), value=TRUE)]
colnames(ranks) <- sub("t_statistic_","",colnames(ranks))
ll = list()
for (i in unique(term2gene$Lipid_Class)){
ll[[i]] = term2gene$lipid[term2gene$Lipid_Class==i]
}
for (i in 1:ncol(ranks)){
print(colnames(ranks)[i])
rank1 <- ranks[,i] %>% pull()
names(rank1) = df$name
fgsea::fgsea(ll,rank1) %>% arrange(pval) %>% print()
}## [1] "X22RV1_PunA5 - X22RV1_PunA2.5"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: TG 0.01768804 0.2653206 0.35248786 0.4678978 1.4831391 71
## 2: DG 0.06161972 0.3439803 0.24133998 0.6524919 1.4985745 13
## 3: CE 0.06879607 0.3439803 0.27128855 -0.5053864 -1.4857340 19
## 4: PC 0.10361068 0.3885400 0.17232434 0.5175377 1.3947970 25
## 5: PC O 0.15529412 0.4658824 0.17232434 -0.4594088 -1.3111253 17
## 6: HexCer 0.21561338 0.5101394 0.12625399 -0.6733624 -1.2125126 4
## 7: CL 0.26197183 0.5101394 0.14290115 -0.3665417 -1.1574089 27
## 8: PS 0.27356322 0.5101394 0.12443417 -0.4678466 -1.1805204 12
## 9: SM 0.30608365 0.5101394 0.10434395 0.5546460 1.1477999 9
## 10: PI 0.53647059 0.7666016 0.08407456 -0.3253511 -0.9285325 17
## 11: PE O 0.57457213 0.7666016 0.08243441 -0.3162005 -0.9122019 18
## 12: Cer 0.61328125 0.7666016 0.06751890 0.4393939 0.8773221 7
## 13: FC 0.76447106 0.8313574 0.05797548 0.6037037 0.8275456 1
## 14: PG 0.77593361 0.8313574 0.05909548 -0.3300315 -0.7454575 8
## 15: PE 0.93667546 0.9366755 0.06157068 -0.2190288 -0.6637744 23
## leadingEdge
## <list>
## 1: TG 50:6,....
## 2: DG 36:3,....
## 3: CE 20:5,....
## 4: PC 36:6,....
## 5: PC O-30:....
## 6: HexCer 4....
## 7: CL 68:2,....
## 8: PS 34:1,....
## 9: SM 40:2;....
## 10: PI 38:6,....
## 11: PE O-36:....
## 12: Cer 34:0....
## 13: FC
## 14: PG 36:2,....
## 15: PE 36:5,....
## [1] "LNCaP_PunA5 - LNCaP_PunA2.5"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: CL 2.930309e-05 0.0004395463 0.57561026 -0.6295429 -2.0045349 27
## 2: PS 1.043993e-03 0.0078299491 0.45505987 0.5984556 1.9994542 12
## 3: Cer 6.066198e-03 0.0303309898 0.40701792 -0.7300125 -1.6831825 7
## 4: HexCer 4.251676e-02 0.1594378662 0.32177592 0.7041199 1.5123511 4
## 5: CE 8.361204e-02 0.2158469945 0.16080140 -0.4541699 -1.3410882 19
## 6: PC 8.633880e-02 0.2158469945 0.15631240 -0.4271694 -1.3438156 25
## 7: DG 1.701292e-01 0.3645625752 0.24924655 0.3682171 1.2662453 13
## 8: TG 3.174442e-01 0.5952079108 0.06767604 -0.2982361 -1.1070412 71
## 9: PC O 3.600451e-01 0.6000752445 0.06674261 -0.3767471 -1.0841386 17
## 10: SM 4.207390e-01 0.6177547285 0.06184060 -0.4251465 -1.0490710 9
## 11: PE O 4.530201e-01 0.6177547285 0.05536428 -0.3526708 -1.0292128 18
## 12: PE 5.577558e-01 0.6971947195 0.04504428 -0.3089253 -0.9532509 23
## 13: FC 6.687500e-01 0.7042889391 0.06643641 0.6629630 0.8916308 1
## 14: PG 7.037944e-01 0.7042889391 0.03927586 -0.3598959 -0.8576105 8
## 15: PI 7.042889e-01 0.7042889391 0.03546650 -0.2995805 -0.8620818 17
## leadingEdge
## <list>
## 1: CL 74:8,....
## 2: PS 40:2,....
## 3: Cer 42:1....
## 4: HexCer 4....
## 5: CE 22:6,....
## 6: PC 30:0,....
## 7: DG 36:4,....
## 8: TG 50:1,....
## 9: PC O-38:....
## 10: SM 41:2;....
## 11: PE O-36:....
## 12: PE 36:1,....
## 13: FC
## 14: PG 36:1,....
## 15: PI 33:0,....
## [1] "PC3_PunA5 - PC3_PunA2.5"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: TG 0.02793873 0.4190809 0.35248786 0.5149708 1.4889561 71
## 2: CL 0.10075567 0.6345178 0.22496609 -0.4951556 -1.3654236 27
## 3: PC 0.12690355 0.6345178 0.16080140 0.5516406 1.3384255 25
## 4: PE O 0.30493274 0.9172662 0.11524000 -0.4637762 -1.1413854 18
## 5: Cer 0.51158645 0.9172662 0.07200331 -0.4923669 -0.9794328 7
## 6: PG 0.56261682 0.9172662 0.06961334 -0.4634563 -0.9415633 8
## 7: SM 0.60266160 0.9172662 0.06705126 -0.4300896 -0.8997723 9
## 8: CE 0.64428312 0.9172662 0.06157068 0.3809290 0.8580853 19
## 9: PS 0.66935484 0.9172662 0.06479434 -0.3838019 -0.8485074 12
## 10: HexCer 0.68840580 0.9172662 0.07235709 0.4794007 0.7980309 4
## 11: PC O 0.73766816 0.9172662 0.06523531 -0.3365565 -0.8116335 17
## 12: DG 0.78362573 0.9172662 0.05571042 0.3452230 0.7365418 13
## 13: PI 0.79496403 0.9172662 0.05132233 0.3232008 0.7222915 17
## 14: PE 0.90192644 0.9663498 0.04432935 0.2768472 0.6547998 23
## 15: FC 0.97308489 0.9730849 0.04850598 0.5148148 0.6934390 1
## leadingEdge
## <list>
## 1: TG 48:3,....
## 2: CL 68:2,....
## 3: PC 36:6,....
## 4: PE O-40:....
## 5: Cer 42:1....
## 6: PG 36:2,....
## 7: SM 34:1;....
## 8: CE 18:3,....
## 9: PS 36:2,....
## 10: HexCer 4....
## 11: PC O-38:....
## 12: DG 36:4,....
## 13: PI 33:0,....
## 14: PE 36:6,....
## 15: FC
## [1] "int_LNCaP_PunA5_PunA2.5"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: PS 0.0001237415 0.001856122 0.51884808 0.6917026 2.4787294 12
## 2: Cer 0.0272146117 0.139772243 0.35248786 -0.7064197 -1.5390086 7
## 3: CL 0.0364415863 0.139772243 0.24504179 -0.4929394 -1.4859594 27
## 4: PE 0.0372725980 0.139772243 0.32177592 0.3306452 1.5242775 23
## 5: PC 0.0745140389 0.223542117 0.16823817 -0.4707110 -1.3995016 25
## 6: PC O 0.1140754881 0.285188720 0.28780513 0.3379820 1.4151680 17
## 7: HexCer 0.1439114391 0.294858871 0.22798720 0.5948784 1.2994432 4
## 8: TG 0.1572580645 0.294858871 0.10632326 -0.3566550 -1.2152523 71
## 9: PG 0.2736196319 0.456032720 0.08528847 -0.5173569 -1.1759909 8
## 10: PI 0.3662131519 0.543850267 0.06613262 -0.4003396 -1.0899624 17
## 11: DG 0.3988235294 0.543850267 0.06378454 -0.4168567 -1.0664725 13
## 12: SM 0.4623786408 0.577973301 0.05822162 -0.4290770 -1.0061553 9
## 13: CE 0.6461366181 0.745542252 0.03911552 -0.3147880 -0.8835361 19
## 14: PE O 0.7018140590 0.751943635 0.03584493 -0.3048277 -0.8394583 18
## 15: FC 0.7819253438 0.781925344 0.05617666 0.6111111 0.8168476 1
## leadingEdge
## <list>
## 1: PS 34:1,....
## 2: Cer 42:1....
## 3: CL 74:8,....
## 4: PE 36:5,....
## 5: PC 38:5,....
## 6: PC O-32:....
## 7: HexCer 4....
## 8: TG 50:6,....
## 9: PG 34:0,....
## 10: PI 33:0
## 11: DG 36:3,....
## 12: SM 41:2;....
## 13: CE 18:3,....
## 14: PE O-32:....
## 15: FC
## [1] "int_PC3_PunA5_PunA2.5"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: CE 0.1425703 0.8752515 0.16565670 0.5903765 1.3176253 19
## 2: DG 0.2047930 0.8752515 0.14205664 -0.6031157 -1.2586862 13
## 3: PC O 0.2295720 0.8752515 0.12503337 0.5525808 1.2121971 17
## 4: CL 0.2334004 0.8752515 0.12625399 -0.4778384 -1.1545422 27
## 5: SM 0.3606557 0.9055300 0.09889030 -0.5555177 -1.1000063 9
## 6: TG 0.3759690 0.9055300 0.09314546 0.3594288 1.0372141 71
## 7: PI 0.5573770 0.9055300 0.07473852 -0.4299371 -0.9430561 17
## 8: PE O 0.5809717 0.9055300 0.07200331 -0.4102419 -0.9135181 18
## 9: PS 0.6355140 0.9055300 0.06364241 0.4182889 0.8671110 12
## 10: Cer 0.6430108 0.9055300 0.06994587 -0.4721275 -0.8760734 7
## 11: PC 0.7349398 0.9055300 0.06011861 -0.3478664 -0.8302265 25
## 12: PG 0.7872340 0.9055300 0.05960370 -0.3779637 -0.7248866 8
## 13: HexCer 0.8185328 0.9055300 0.05323865 0.4268886 0.6892015 4
## 14: FC 0.8451613 0.9055300 0.05676724 -0.5814815 -0.7787898 1
## 15: PE 0.9979839 0.9979839 0.04613792 -0.2099017 -0.4936754 23
## leadingEdge
## <list>
## 1: CE 22:4,....
## 2: DG 40:6,....
## 3: PC O-36:....
## 4: CL 70:3,....
## 5: SM 40:2;....
## 6: TG 55:2,....
## 7: PI 40:5,....
## 8: PE O-40:....
## 9: PS 34:1,....
## 10: Cer 42:1....
## 11: PC 40:8,....
## 12: PG 40:7
## 13: HexCer 4....
## 14: FC
## 15: PE 34:3,....2.6 PunA 5 vs PunA 10
2.6.1 DEA
all the lipids
only lipids not totally missing in ome condition
upregulated
if(sum(colSums(res$us_pos)>0)>1){
UpSetR::upset(as.data.frame(res$us_pos),
keep.order = TRUE, nsets = ncol(res$us_pos)) %>%
print()
}
downregulated
if(sum(colSums(res$us_neg)>0)>1){
UpSetR::upset(as.data.frame(res$us_neg),
keep.order = TRUE, nsets = ncol(res$us_neg)) %>%
print()
}
#### heatmaps
ht_fun()## [[1]]
##
## [[2]]
##
## [[3]]
2.6.2 GSEA
GSEA
df = res$df_all_wide
ranks <- df[,grep("t_statistic_",colnames(df), value=TRUE)]
colnames(ranks) <- sub("t_statistic_","",colnames(ranks))
ll = list()
for (i in unique(term2gene$Lipid_Class)){
ll[[i]] = term2gene$lipid[term2gene$Lipid_Class==i]
}
for (i in 1:ncol(ranks)){
print(colnames(ranks)[i])
rank1 <- ranks[,i] %>% pull()
names(rank1) = df$name
fgsea::fgsea(ll,rank1) %>% arrange(pval) %>% print()
}## [1] "X22RV1_PunA10 - X22RV1_PunA5"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: PE 0.04604317 0.6675697 0.25296112 0.6331415 1.4455158 23
## 2: CE 0.13455657 0.6675697 0.21392786 -0.5261684 -1.2799392 19
## 3: TG 0.13636364 0.6675697 0.27650060 -0.3709659 -1.1652914 71
## 4: DG 0.17801858 0.6675697 0.12687573 0.6168463 1.2583043 13
## 5: PG 0.29048843 0.7521008 0.12814292 -0.6146720 -1.2043045 8
## 6: HexCer 0.30084034 0.7521008 0.09787733 0.7231008 1.1487494 4
## 7: PI 0.43093093 0.7737342 0.07200331 0.4843955 1.0365309 17
## 8: PC O 0.49099099 0.7737342 0.06553210 0.4649608 0.9949436 17
## 9: PS 0.49388379 0.7737342 0.06613262 0.4909145 0.9937467 12
## 10: SM 0.51582278 0.7737342 0.06568136 0.5162848 0.9769850 9
## 11: PC 0.57803468 0.7882291 0.05594286 0.4039383 0.9251089 25
## 12: CL 0.82293179 0.9832572 0.03992186 0.3289254 0.7626674 27
## 13: FC 0.86868687 0.9832572 0.05258990 0.5555556 0.7585840 1
## 14: Cer 0.97039474 0.9832572 0.03808421 0.2881184 0.5157964 7
## 15: PE O 0.98325723 0.9832572 0.03382454 0.2573323 0.5490385 18
## leadingEdge
## <list>
## 1: PE 36:6,....
## 2: CE 22:3,....
## 3: TG 57:4,....
## 4: DG 36:3,....
## 5: PG 40:7
## 6: HexCer 3....
## 7: PI 33:0,....
## 8: PC O-32:....
## 9: PS 34:1,....
## 10: SM 32:1;....
## 11: PC 36:6,....
## 12: CL 68:2,....
## 13: FC
## 14: Cer 34:1....
## 15: PE O-36:....
## [1] "LNCaP_PunA10 - LNCaP_PunA5"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: PS 0.0000264279 0.0003964185 0.57561026 -0.6911197 -2.5314119 12
## 2: CL 0.0001296559 0.0009724191 0.51884808 0.6066332 1.8839582 27
## 3: Cer 0.0005926738 0.0029633689 0.47727082 0.8130607 1.9026279 7
## 4: DG 0.0014209013 0.0053283800 0.45505987 -0.5426357 -2.0429779 13
## 5: HexCer 0.0159809059 0.0400265919 0.35248786 -0.7677903 -1.6867483 4
## 6: PC 0.0160106368 0.0400265919 0.35248786 0.4915515 1.5110982 25
## 7: PE 0.0335025381 0.0717911530 0.24891111 0.4907735 1.4908429 23
## 8: TG 0.1490000000 0.2793750000 0.10925004 0.3469667 1.1878954 71
## 9: CE 0.3615934627 0.6026557712 0.06170541 0.3661876 1.0785180 19
## 10: SM 0.4850828729 0.7276243094 0.05163560 0.3947621 0.9859881 9
## 11: FC 0.6047430830 0.7790041068 0.06879431 -0.7148148 -0.9459499 1
## 12: PE O 0.6232032854 0.7790041068 0.03676196 0.3109289 0.9078533 18
## 13: PC O 0.8436853002 0.9302587177 0.02179600 0.2562050 0.7355379 17
## 14: PI 0.8995859213 0.9302587177 0.01778148 0.2323477 0.6670461 17
## 15: PG 0.9302587177 0.9302587177 0.02092759 0.2535506 0.6118348 8
## leadingEdge
## <list>
## 1: PS 32:1,....
## 2: CL 70:7,....
## 3: Cer 42:1....
## 4: DG 36:5,....
## 5: HexCer 4....
## 6: PC 36:6,....
## 7: PE 36:6,....
## 8: TG 48:3,....
## 9: CE 18:3,....
## 10: SM 34:1;....
## 11: FC
## 12: PE O-36:....
## 13: PC O-38:....
## 14: PI 33:0,....
## 15: PG 36:1,....
## [1] "PC3_PunA10 - PC3_PunA5"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: TG 0.006097029 0.0793187 0.40701792 0.5984495 1.4202756 71
## 2: PS 0.010575827 0.0793187 0.38073040 -0.6273195 -1.7521265 12
## 3: DG 0.056581986 0.2785486 0.20207171 0.7088503 1.4170612 13
## 4: PG 0.085556917 0.2785486 0.28780513 -0.5855513 -1.4186377 8
## 5: CL 0.092849520 0.2785486 0.14826150 0.5968873 1.3218457 27
## 6: PE 0.276387378 0.6744910 0.07829552 0.5351109 1.1611726 23
## 7: HexCer 0.357377049 0.6744910 0.13077714 -0.5505618 -1.0989417 4
## 8: PI 0.359728507 0.6744910 0.06689663 0.5373283 1.1171497 17
## 9: CE 0.459579181 0.7659653 0.05423159 0.4932902 1.0492615 19
## 10: Cer 0.660759494 0.9318182 0.04371258 0.4900302 0.8828160 7
## 11: PE O 0.683333333 0.9318182 0.03614919 0.4169693 0.8807780 18
## 12: SM 0.751507841 0.9322382 0.03554202 0.4138641 0.7891837 9
## 13: PC 0.883172562 0.9322382 0.02114564 0.3108592 0.6833074 25
## 14: PC O 0.915158371 0.9322382 0.02222678 0.2895461 0.6019901 17
## 15: FC 0.932238193 0.9322382 0.05009229 -0.5370370 -0.7140758 1
## leadingEdge
## <list>
## 1: TG 50:6,....
## 2: PS 40:5,....
## 3: DG 40:6,....
## 4: PG 40:8,....
## 5: CL 56:3,....
## 6: PE 36:6,....
## 7: HexCer 4....
## 8: PI 33:0,....
## 9: CE 22:3,....
## 10: Cer 40:1....
## 11: PE O-36:....
## 12: SM 42:2;....
## 13: PC 36:6,....
## 14: PC O-36:....
## 15: FC
## [1] "int_LNCaP_PunA10_PunA5"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: TG 0.0002301273 0.003451909 0.51884808 0.5139198 1.6416738 71
## 2: DG 0.0008606684 0.006455013 0.47727082 -0.5503876 -2.0130706 13
## 3: Cer 0.0021593624 0.010796812 0.43170770 0.7961750 1.7625255 7
## 4: PS 0.0042426702 0.015910013 0.40701792 -0.5200601 -1.8354057 12
## 5: CL 0.0060833590 0.018250077 0.40701792 0.5355752 1.5734883 27
## 6: HexCer 0.0709183181 0.177295795 0.28780513 -0.6666667 -1.4781027 4
## 7: CE 0.1930745016 0.413731075 0.09624060 0.4369748 1.2075086 19
## 8: PC 0.2615384615 0.490384615 0.07808923 0.3969829 1.1440441 25
## 9: FC 0.6891891892 0.870011403 0.06130261 -0.6481481 -0.8776738 1
## 10: PE 0.7079918033 0.870011403 0.03056081 0.3064005 0.8769002 23
## 11: PG 0.7725674091 0.870011403 0.03287010 0.3416757 0.7810995 8
## 12: PE O 0.7731092437 0.870011403 0.02737773 0.2999299 0.8204281 18
## 13: PC O 0.8250790306 0.870011403 0.02407421 0.2822584 0.7641233 17
## 14: PI 0.8387776607 0.870011403 0.02315383 0.2759757 0.7471151 17
## 15: SM 0.8700114025 0.870011403 0.02548206 0.2959317 0.7004719 9
## leadingEdge
## <list>
## 1: TG 60:5,....
## 2: DG 34:2,....
## 3: Cer 42:1....
## 4: PS 34:1,....
## 5: CL 70:7,....
## 6: HexCer 4....
## 7: CE 22:3,....
## 8: PC 38:3,....
## 9: FC
## 10: PE 36:4,....
## 11: PG 36:1,....
## 12: PE O-36:....
## 13: PC O-38:....
## 14: PI 33:0,....
## 15: SM 34:1;....
## [1] "int_PC3_PunA10_PunA5"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: TG 0.004250279 0.06375419 0.40701792 0.5261124 1.5461360 71
## 2: PS 0.013780186 0.10335139 0.38073040 -0.6693306 -1.6737990 12
## 3: CL 0.032427601 0.16213801 0.32177592 0.5889977 1.5253923 27
## 4: PE 0.150943396 0.56603774 0.22496609 -0.4313120 -1.2826618 23
## 5: CE 0.316970547 0.93605547 0.08455574 0.4725717 1.1260073 19
## 6: HexCer 0.456852792 0.93605547 0.09754492 -0.5374202 -0.9961369 4
## 7: DG 0.468053492 0.93605547 0.06736239 0.4638120 1.0188832 13
## 8: PC O 0.597972973 0.93605547 0.09854998 -0.3267717 -0.9005290 17
## 9: PG 0.627300613 0.93605547 0.05490737 0.4499364 0.8905052 8
## 10: Cer 0.651632970 0.93605547 0.05378728 0.4533622 0.8733892 7
## 11: SM 0.686440678 0.93605547 0.08063885 -0.3475621 -0.8119522 9
## 12: FC 0.819253438 0.96742210 0.05400883 -0.5814815 -0.7823204 1
## 13: PC 0.841698842 0.96742210 0.08653997 -0.2476985 -0.7570231 25
## 14: PE O 0.938375350 0.96742210 0.03207042 0.2555041 0.6058603 18
## 15: PI 0.967422096 0.96742210 0.03113351 0.2366085 0.5534791 17
## leadingEdge
## <list>
## 1: TG 53:1,....
## 2: PS 34:1,....
## 3: CL 56:3,....
## 4: PE 36:6,....
## 5: CE 22:3,....
## 6: HexCer 4....
## 7: DG 36:1,....
## 8: PC O-32:....
## 9: PG 40:7,....
## 10: Cer 40:1....
## 11: SM 32:1;....
## 12: FC
## 13: PC 38:5,....
## 14: PE O-40:....
## 15: PI 36:4,....2.7 PunA 2.5 vs PunA 10
2.7.1 DEA
all the lipids
only lipids not totally missing in ome condition
upregulated
if(sum(colSums(res$us_pos)>0)>1){
UpSetR::upset(as.data.frame(res$us_pos),
keep.order = TRUE, nsets = ncol(res$us_pos)) %>%
print()
}
downregulated
if(sum(colSums(res$us_neg)>0)>1){
UpSetR::upset(as.data.frame(res$us_neg),
keep.order = TRUE, nsets = ncol(res$us_neg)) %>%
print()
}
#### heatmaps
ht_fun()## [[1]]
##
## [[2]]
##
## [[3]]
2.7.2 GSEA
GSEA
df = res$df_all_wide
ranks <- df[,grep("t_statistic_",colnames(df), value=TRUE)]
colnames(ranks) <- sub("t_statistic_","",colnames(ranks))
ll = list()
for (i in unique(term2gene$Lipid_Class)){
ll[[i]] = term2gene$lipid[term2gene$Lipid_Class==i]
}
for (i in 1:ncol(ranks)){
print(colnames(ranks)[i])
rank1 <- ranks[,i] %>% pull()
names(rank1) = df$name
fgsea::fgsea(ll,rank1) %>% arrange(pval) %>% print()
}## [1] "X22RV1_PunA10 - X22RV1_PunA2.5"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: DG 0.02740575 0.3226300 0.35248786 0.7732116 1.4949251 13
## 2: CE 0.04301733 0.3226300 0.32177592 -0.6114447 -1.5250312 19
## 3: PE 0.13731343 0.5978261 0.14375899 0.6001651 1.3186407 23
## 4: PC 0.15942029 0.5978261 0.13010563 0.5731926 1.2879251 25
## 5: TG 0.28641975 0.8557348 0.08312913 0.4333582 1.1173418 71
## 6: SM 0.34229391 0.8557348 0.09405035 0.6148086 1.1209480 9
## 7: PI 0.48661417 0.9701671 0.06831109 0.4828796 0.9977994 17
## 8: PG 0.52808989 0.9701671 0.08243441 -0.5009029 -0.9819042 8
## 9: PC O 0.61852861 0.9701671 0.08431443 -0.3667234 -0.8828480 17
## 10: PS 0.64677804 0.9701671 0.07492788 -0.3899754 -0.8620031 12
## 11: FC 0.72000000 0.9768212 0.06090393 0.6407407 0.8638488 1
## 12: CL 0.78145695 0.9768212 0.08220549 -0.3143448 -0.8401887 27
## 13: HexCer 0.87550201 0.9810427 0.05195125 -0.4078310 -0.6886026 4
## 14: Cer 0.95596330 0.9810427 0.04380020 0.2878788 0.5057712 7
## 15: PE O 0.98104265 0.9810427 0.03569331 0.2379388 0.4937560 18
## leadingEdge
## <list>
## 1: DG 36:3,....
## 2: CE 22:4,....
## 3: PE 36:6,....
## 4: PC 36:6,....
## 5: TG 50:6,....
## 6: SM 41:1;....
## 7: PI 33:0,....
## 8: PG 36:2,....
## 9: PC O-34:....
## 10: PS 36:2,....
## 11: FC
## 12: CL 74:9,....
## 13: HexCer 4....
## 14: Cer 34:1....
## 15: PE O-32:....
## [1] "LNCaP_PunA10 - LNCaP_PunA2.5"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: TG 7.155452e-06 0.0001073318 0.61052688 0.7937091 1.4516776 71
## 2: PE 1.764092e-01 0.9908735332 0.10135074 0.6969108 1.2236432 23
## 3: PS 3.203991e-01 0.9908735332 0.07165274 0.6864148 1.1476218 12
## 4: PC 3.601651e-01 0.9908735332 0.06238615 0.6246642 1.1037790 25
## 5: CE 3.849417e-01 0.9908735332 0.06050930 0.6347571 1.1040608 19
## 6: PG 5.095541e-01 0.9908735332 0.15524197 -0.3802281 -0.9211616 8
## 7: Cer 5.119617e-01 0.9908735332 0.05280500 0.6278543 1.0113647 7
## 8: SM 7.025463e-01 0.9908735332 0.03676196 0.5211008 0.8497804 9
## 9: PC O 7.813853e-01 0.9908735332 0.02836285 0.4677294 0.8015363 17
## 10: FC 8.483685e-01 0.9908735332 0.05132233 0.5925926 0.7764019 1
## 11: PI 8.787879e-01 0.9908735332 0.02201165 0.3845751 0.6590369 17
## 12: PE O 9.023605e-01 0.9908735332 0.01990018 0.3650860 0.6299736 18
## 13: DG 9.117971e-01 0.9908735332 0.02092759 0.3538809 0.5958892 13
## 14: CL 9.804325e-01 0.9908735332 0.01035046 0.2778620 0.4939179 27
## 15: HexCer 9.908735e-01 0.9908735332 0.02569275 0.3018858 0.4539483 4
## leadingEdge
## <list>
## 1: TG 50:6,....
## 2: PE 36:6,....
## 3: PS 40:2,....
## 4: PC 36:6,....
## 5: CE 18:3,....
## 6: PG 40:7,....
## 7: Cer 34:1....
## 8: SM 41:1;....
## 9: PC O-36:....
## 10: FC
## 11: PI 38:6,....
## 12: PE O-34:....
## 13: DG 36:4,....
## 14: CL 70:7,....
## 15: HexCer 3....
## [1] "PC3_PunA10 - PC3_PunA2.5"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: TG 0.02862986 0.2916192 0.27128855 0.6299885 1.3486739 71
## 2: DG 0.03888256 0.2916192 0.32177592 0.7990657 1.4495970 13
## 3: PS 0.11673152 0.5732205 0.26166352 -0.6182647 -1.3721205 12
## 4: PC 0.15285881 0.5732205 0.11776579 0.6376858 1.2693041 25
## 5: PG 0.22121212 0.6636364 0.16318013 -0.6108197 -1.2532315 8
## 6: PE 0.35335689 0.8833922 0.06977925 0.5667765 1.1152909 23
## 7: CE 0.56127451 0.9852941 0.04969014 0.4971894 0.9547910 19
## 8: PI 0.66040100 0.9852941 0.04327687 0.4644897 0.8816940 17
## 9: CL 0.76064442 0.9852941 0.03272419 0.4047967 0.8112092 27
## 10: SM 0.81037037 0.9852941 0.04157179 0.4274809 0.7321813 9
## 11: HexCer 0.87979094 0.9852941 0.04522474 0.4307116 0.6602691 4
## 12: FC 0.91752577 0.9852941 0.05101141 0.5333333 0.7181527 1
## 13: PE O 0.92346939 0.9852941 0.09721508 -0.2529644 -0.6486099 18
## 14: Cer 0.96788991 0.9852941 0.03479041 0.2962148 0.4925741 7
## 15: PC O 0.98529412 0.9852941 0.09110731 -0.2007874 -0.5074900 17
## leadingEdge
## <list>
## 1: TG 54:7,....
## 2: DG 38:4,....
## 3: PS 40:5,....
## 4: PC 36:6,....
## 5: PG 34:1,....
## 6: PE 36:6,....
## 7: CE 18:3,....
## 8: PI 33:0,....
## 9: CL 72:11....
## 10: SM 41:1;....
## 11: HexCer 3....
## 12: FC
## 13: PE O-40:....
## 14: Cer 40:1....
## 15: PC O-40:....
## [1] "int_LNCaP_PunA10_PunA2.5"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: DG 0.0004708108 0.007062162 0.49849311 -0.7812177 -1.9842108 13
## 2: TG 0.0020919566 0.012230566 0.43170770 0.5137722 1.5803462 71
## 3: CE 0.0024461131 0.012230566 0.43170770 0.7080947 1.7228821 19
## 4: PC O 0.2376373626 0.891140110 0.09992770 0.5122230 1.2087106 17
## 5: PS 0.4102209945 0.957295035 0.07028128 0.4780539 1.0668702 12
## 6: PI 0.5036496350 0.957295035 0.11426650 -0.3556758 -0.9780662 17
## 7: PG 0.5563480742 0.957295035 0.05712585 0.4837988 0.9644265 8
## 8: Cer 0.6521739130 0.957295035 0.05049830 0.4614737 0.8869278 7
## 9: SM 0.7384105960 0.957295035 0.08528847 -0.3319832 -0.7743484 9
## 10: FC 0.8146551724 0.957295035 0.05859376 -0.5851852 -0.7885778 1
## 11: PE O 0.8208955224 0.957295035 0.08603472 -0.2759340 -0.7792920 18
## 12: PC 0.8427419355 0.957295035 0.08889453 -0.2490994 -0.7606706 25
## 13: HexCer 0.8571428571 0.957295035 0.07182763 -0.3595506 -0.6744654 4
## 14: PE 0.8934753662 0.957295035 0.03199805 0.2671823 0.6809261 23
## 15: CL 0.9960369881 0.996036988 0.02611396 0.1779512 0.4716567 27
## leadingEdge
## <list>
## 1: DG 36:3,....
## 2: TG 57:4,....
## 3: CE 22:4,....
## 4: PC O-36:....
## 5: PS 36:2,....
## 6: PI 33:0
## 7: PG 36:2,....
## 8: Cer 42:2....
## 9: SM 41:2;....
## 10: FC
## 11: PE O-32:....
## 12: PC 38:5,....
## 13: HexCer 4....
## 14: PE 36:4,....
## 15: CL 70:7,....
## [1] "int_PC3_PunA10_PunA2.5"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: CE 0.004757594 0.07136391 0.40701792 0.7400766 1.6489872 19
## 2: PE 0.028682525 0.18564356 0.35248786 -0.5648697 -1.5262576 23
## 3: TG 0.037128713 0.18564356 0.26166352 0.5001878 1.3777776 71
## 4: SM 0.185000000 0.69375000 0.16197895 -0.6013221 -1.3030862 9
## 5: PS 0.236559140 0.70967742 0.14733121 -0.5169142 -1.1876109 12
## 6: CL 0.336661912 0.84165478 0.08220549 0.4580775 1.0967063 27
## 7: Cer 0.534109817 0.92953930 0.06643641 0.5177195 0.9525866 7
## 8: PC 0.583333333 0.92953930 0.09688777 -0.3433671 -0.9308295 25
## 9: PC O 0.734824281 0.92953930 0.04949049 0.3770094 0.8200496 17
## 10: PE O 0.764542936 0.92953930 0.07399014 -0.3175755 -0.8093322 18
## 11: PI 0.779255319 0.92953930 0.07096095 -0.3104145 -0.7900577 17
## 12: FC 0.844230769 0.92953930 0.05163560 -0.5851852 -0.7824544 1
## 13: HexCer 0.915331808 0.92953930 0.05594286 -0.3224888 -0.5673245 4
## 14: PG 0.917922948 0.92953930 0.04148804 0.3269962 0.6145817 8
## 15: DG 0.929539295 0.92953930 0.06321912 -0.2780362 -0.6510141 13
## leadingEdge
## <list>
## 1: CE 22:3,....
## 2: PE 34:3,....
## 3: TG 53:1,....
## 4: SM 41:1;....
## 5: PS 40:5,....
## 6: CL 72:11....
## 7: Cer 40:1....
## 8: PC 40:8,....
## 9: PC O-36:....
## 10: PE O-32:....
## 11: PI 40:5,....
## 12: FC
## 13: HexCer 4....
## 14: PG 36:0,....
## 15: DG 36:3,....2.8 CTL vs Fer1_3
2.8.1 DEA
all the lipids
only lipids not totally missing in ome condition
upregulated
if(sum(colSums(res$us_pos)>0)>1){
UpSetR::upset(as.data.frame(res$us_pos),
keep.order = TRUE, nsets = ncol(res$us_pos)) %>%
print()
}
pander("downregulated")downregulated
if(sum(colSums(res$us_neg)>0)>1){
UpSetR::upset(as.data.frame(res$us_neg),
keep.order = TRUE, nsets = ncol(res$us_neg)) %>%
print()
}
## [[1]]
##
## [[2]]
##
## [[3]]
2.8.2 GSEA
GSEA
df = res$df_all_wide
ranks <- df[,grep("t_statistic_",colnames(df), value=TRUE)]
colnames(ranks) <- sub("t_statistic_","",colnames(ranks))
ll = list()
for (i in unique(term2gene$Lipid_Class)){
ll[[i]] = term2gene$lipid[term2gene$Lipid_Class==i]
}
for (i in 1:ncol(ranks)){
print(colnames(ranks)[i])
rank1 <- ranks[,i] %>% pull()
names(rank1) = df$name
fgsea::fgsea(ll,rank1) %>% arrange(pval) %>% print()
}## [1] "X22RV1_Fer1_3 - X22RV1_Control"## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 1 pathways for which P-values were not calculated
## properly due to unbalanced (positive and negative) gene-level statistic values.
## For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple
## = 10000)## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: PE 2.795299e-05 0.0003913419 0.57561026 -0.6665788 -1.9235402 23
## 2: PC 4.038910e-03 0.0282723709 0.40701792 -0.5502086 -1.6171011 25
## 3: PE O 1.381821e-02 0.0611559140 0.38073040 -0.5452961 -1.5172941 18
## 4: Cer 2.177677e-02 0.0611559140 0.35248786 -0.6988921 -1.5662568 7
## 5: HexCer 2.577807e-02 0.0611559140 0.35248786 0.7490637 1.6406582 4
## 6: CL 2.620968e-02 0.0611559140 0.28201335 -0.4936695 -1.4690734 27
## 7: PI 8.263598e-02 0.1652719665 0.15631240 -0.4905453 -1.3466242 17
## 8: PG 1.539326e-01 0.2693820225 0.11475072 -0.5451488 -1.2730257 8
## 9: PS 3.117479e-01 0.4849411330 0.20350896 0.3043776 1.1070416 12
## 10: SM 5.593407e-01 0.7830769231 0.04486451 -0.3922466 -0.9472588 9
## 11: DG 6.666667e-01 0.8257161249 0.03516523 -0.3360732 -0.8794835 13
## 12: CE 7.373841e-01 0.8257161249 0.02878615 -0.2992775 -0.8415487 19
## 13: PC O 7.667364e-01 0.8257161249 0.02758859 -0.3023988 -0.8301324 17
## 14: FC 8.789062e-01 0.8789062500 0.05049830 0.5629630 0.7513470 1
## 15: TG NA NA NA 0.3555144 NA 71
## leadingEdge
## <list>
## 1: PE 36:4,....
## 2: PC 36:3,....
## 3: PE O-40:....
## 4: Cer 42:2....
## 5: HexCer 4....
## 6: CL 70:6,....
## 7: PI 36:4,....
## 8: PG 34:0,....
## 9: PS 38:0,....
## 10: SM 34:1;....
## 11: DG 36:4,....
## 12: CE 22:6,....
## 13: PC O-36:....
## 14: FC
## 15: TG 60:5,....
## [1] "LNCaP_Fer1_3 - LNCaP_Control"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: TG 3.603590e-19 5.405385e-18 1.13308993 0.8304835 2.5282813 71
## 2: PE 1.667790e-04 1.250842e-03 0.51884808 -0.7785036 -1.9030562 23
## 3: DG 6.701139e-03 3.350569e-02 0.40701792 -0.8065339 -1.7755818 13
## 4: HexCer 1.473684e-01 5.526316e-01 0.18820415 -0.7722596 -1.4459316 4
## 5: SM 2.485549e-01 7.456647e-01 0.14920754 -0.6199468 -1.2732483 9
## 6: CE 2.989045e-01 7.472613e-01 0.09405035 0.4730384 1.1345176 19
## 7: PE O 5.079787e-01 9.443489e-01 0.09405035 -0.4144005 -0.9776109 18
## 8: Cer 5.878788e-01 9.443489e-01 0.05736674 0.4828838 0.9383108 7
## 9: PG 6.548673e-01 9.443489e-01 0.08553569 -0.4024174 -0.8116003 8
## 10: PC O 7.727273e-01 9.443489e-01 0.07165274 -0.3146788 -0.7386052 17
## 11: FC 7.762646e-01 9.443489e-01 0.05605959 0.6148148 0.8355049 1
## 12: PS 7.860465e-01 9.443489e-01 0.04504428 0.3529297 0.7705068 12
## 13: CL 8.184358e-01 9.443489e-01 0.07096095 -0.2971740 -0.7497634 27
## 14: PC 8.948787e-01 9.587986e-01 0.06479434 -0.2697317 -0.6718621 25
## 15: PI 9.824841e-01 9.824841e-01 0.03599689 0.2086614 0.4904916 17
## leadingEdge
## <list>
## 1: TG 48:0,....
## 2: PE 34:3,....
## 3: DG 34:2,....
## 4: HexCer 3....
## 5: SM 41:2;....
## 6: CE 18:3,....
## 7: PE O-36:....
## 8: Cer 42:1....
## 9: PG 40:7,....
## 10: PC O-38:....
## 11: FC
## 12: PS 36:3,....
## 13: CL 70:7
## 14: PC 34:4,....
## 15: PI 38:6,....
## [1] "PC3_Fer1_3 - PC3_Control"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: PI 0.03248260 0.2348993 0.27128855 -0.5623084 -1.4620240 17
## 2: Cer 0.03832335 0.2348993 0.25296112 -0.6762365 -1.4511001 7
## 3: PE 0.04697987 0.2348993 0.21925035 -0.5088131 -1.4091087 23
## 4: CE 0.12086659 0.4532497 0.13284630 -0.4833955 -1.2833361 19
## 5: HexCer 0.21875000 0.6464800 0.20207171 0.6067416 1.2057143 4
## 6: PC 0.26415094 0.6464800 0.08175156 -0.4085900 -1.1539561 25
## 7: SM 0.31672598 0.6464800 0.07569463 -0.4874928 -1.1008825 9
## 8: CL 0.34478936 0.6464800 0.06799226 -0.3832587 -1.0936725 27
## 9: PC O 0.58932715 0.9693718 0.04495431 -0.3596929 -0.9352158 17
## 10: PG 0.71770335 0.9693718 0.03730298 -0.3853630 -0.8468303 8
## 11: PE O 0.73156682 0.9693718 0.03464102 -0.3258227 -0.8561621 18
## 12: FC 0.78685259 0.9693718 0.05652995 0.6111111 0.8213115 1
## 13: TG 0.84012220 0.9693718 0.02107303 -0.2356512 -0.7792338 71
## 14: DG 0.96871379 0.9918605 0.02026911 -0.2403245 -0.5879715 13
## 15: PS 0.99186047 0.9918605 0.01900420 -0.2066719 -0.4955581 12
## leadingEdge
## <list>
## 1: PI 38:4,....
## 2: Cer 42:2....
## 3: PE 36:1,....
## 4: CE 20:0,....
## 5: HexCer 4....
## 6: PC 30:0,....
## 7: SM 41:2;....
## 8: CL 70:6,....
## 9: PC O-36:....
## 10: PG 34:1,....
## 11: PE O-40:....
## 12: FC
## 13: TG 55:5,....
## 14: DG 38:5,....
## 15: PS 36:2
## [1] "int_LNCaP_Fer1_3_Control"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: Cer 0.004465064 0.03853645 0.40701792 0.7173976 1.6590276 7
## 2: HexCer 0.005138193 0.03853645 0.40701792 -0.8539326 -1.8532582 4
## 3: PE O 0.012671673 0.06335836 0.38073040 0.5281990 1.5741765 18
## 4: PC 0.029787234 0.11170213 0.27128855 0.4617002 1.4856800 25
## 5: PE 0.163499645 0.49049893 0.24924655 -0.2765705 -1.2486409 23
## 6: PI 0.233223322 0.58305831 0.08809450 0.4064524 1.1916632 17
## 7: CL 0.273594910 0.58627481 0.07747675 0.3518093 1.1438294 27
## 8: PG 0.417582418 0.78296703 0.06335970 0.4331415 1.0349802 8
## 9: TG 0.516194332 0.86032389 0.04477489 0.2643140 0.9905761 71
## 10: CE 0.668484188 0.90887290 0.03630185 0.2928067 0.8811959 19
## 11: DG 0.722222222 0.90887290 0.14463053 -0.2223492 -0.8300776 13
## 12: PS 0.837037037 0.90887290 0.12814292 -0.2098846 -0.7516570 12
## 13: FC 0.880808081 0.90887290 0.05195125 -0.5629630 -0.7598923 1
## 14: PC O 0.887788779 0.90887290 0.02236993 0.2345392 0.6876369 17
## 15: SM 0.908872902 0.90887290 0.02583317 0.2733986 0.6770148 9
## leadingEdge
## <list>
## 1: Cer 42:2....
## 2: HexCer 4....
## 3: PE O-40:....
## 4: PC 32:0,....
## 5: PE 34:3,....
## 6: PI 36:4,....
## 7: CL 74:8,....
## 8: PG 34:0,....
## 9: TG 50:3,....
## 10: CE 22:6,....
## 11: DG 36:3,....
## 12: PS 38:2,....
## 13: FC
## 14: PC O-40:....
## 15: SM 36:1;....
## [1] "int_PC3_Fer1_3_Control"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: CE 0.003615347 0.0542302 0.43170770 -0.6538543 -1.8244952 19
## 2: HexCer 0.020615374 0.1546153 0.35248786 -0.8801498 -1.5628306 4
## 3: PS 0.071611253 0.3580563 0.27128855 -0.5931533 -1.4531728 12
## 4: CL 0.126878130 0.4757930 0.15964670 0.4714966 1.2989971 27
## 5: PC 0.243506494 0.5809979 0.10882013 0.4274887 1.1670458 25
## 6: DG 0.260229133 0.5809979 0.10512513 0.4964400 1.1681965 13
## 7: TG 0.271132376 0.5809979 0.10099059 0.3308976 1.1151752 71
## 8: PE O 0.432835821 0.7753759 0.07687367 0.4041464 1.0306591 18
## 9: PG 0.515248796 0.7753759 0.06643641 0.4747103 0.9842550 8
## 10: PE 0.518151815 0.7753759 0.06751890 0.3658493 0.9773946 23
## 11: Cer 0.572567783 0.7753759 0.06103637 0.4672571 0.9389868 7
## 12: SM 0.656992084 0.7753759 0.07934350 -0.3871494 -0.8728949 9
## 13: PI 0.703947368 0.7753759 0.05280500 0.3311355 0.8331472 17
## 14: PC O 0.723684211 0.7753759 0.05153091 0.3214479 0.8087726 17
## 15: FC 0.828629032 0.8286290 0.05468085 0.5814815 0.7870113 1
## leadingEdge
## <list>
## 1: CE 20:0,....
## 2: HexCer 4....
## 3: PS 38:0,....
## 4: CL 68:5,....
## 5: PC 40:5,....
## 6: DG 36:4,....
## 7: TG 53:4,....
## 8: PE O-40:....
## 9: PG 36:0,....
## 10: PE 36:6,....
## 11: Cer 42:2....
## 12: SM 41:2;....
## 13: PI 40:5,....
## 14: PC O-38:....
## 15: FC2.9 PunA 10 vs Fer1 - PunA 10
2.9.1 DEA
all the lipids
only lipids not totally missing in ome condition
upregulated
if(sum(colSums(res$us_pos)>0)>1){
UpSetR::upset(as.data.frame(res$us_pos),
keep.order = TRUE, nsets = ncol(res$us_pos)) %>%
print()
}
pander("downregulated")downregulated
if(sum(colSums(res$us_neg)>0)>1){
UpSetR::upset(as.data.frame(res$us_neg),
keep.order = TRUE, nsets = ncol(res$us_neg)) %>%
print()
}
## [[1]]
##
## [[2]]
##
## [[3]]
2.9.2 GSEA
GSEA
df = res$df_all_wide
ranks <- df[,grep("t_statistic_",colnames(df), value=TRUE)]
colnames(ranks) <- sub("t_statistic_","",colnames(ranks))
ll = list()
for (i in unique(term2gene$Lipid_Class)){
ll[[i]] = term2gene$lipid[term2gene$Lipid_Class==i]
}
for (i in 1:ncol(ranks)){
print(colnames(ranks)[i])
rank1 <- ranks[,i] %>% pull()
names(rank1) = df$name
fgsea::fgsea(ll,rank1) %>% arrange(pval) %>% print()
}## [1] "X22RV1_PunA10_fer13 - X22RV1_PunA10"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: TG 1.528555e-21 2.292833e-20 1.20397524 0.8429649 2.4058946 71
## 2: PC O 1.054313e-02 7.907346e-02 0.38073040 -0.4922661 -1.7109521 17
## 3: PC 3.796123e-02 1.898062e-01 0.32177592 -0.3455285 -1.3351384 25
## 4: CE 1.079607e-01 4.048528e-01 0.13802224 0.5355880 1.3098422 19
## 5: HexCer 1.548673e-01 4.646018e-01 0.24133998 -0.6709163 -1.3354546 4
## 6: CL 2.877336e-01 7.193339e-01 0.20350896 -0.2622951 -1.0461406 27
## 7: PE O 4.470588e-01 8.682159e-01 0.23112671 -0.2806324 -0.9913283 18
## 8: SM 4.630485e-01 8.682159e-01 0.05582647 0.4815437 1.0171304 9
## 9: FC 5.268595e-01 8.780992e-01 0.07808923 -0.7222222 -0.9840356 1
## 10: PI 7.690608e-01 9.316272e-01 0.03020419 0.3437652 0.8166675 17
## 11: Cer 7.863962e-01 9.316272e-01 0.03287010 0.3982315 0.7926673 7
## 12: PS 8.206583e-01 9.316272e-01 0.02829237 0.3440827 0.7653729 12
## 13: DG 8.605442e-01 9.316272e-01 0.02576296 0.3250150 0.7307851 13
## 14: PE 8.695187e-01 9.316272e-01 0.02193983 0.2895713 0.7298580 23
## 15: PG 9.662398e-01 9.662398e-01 0.02070884 0.2909997 0.6000948 8
## leadingEdge
## <list>
## 1: TG 48:2,....
## 2: PC O-30:....
## 3: PC 38:5,....
## 4: CE 20:5,....
## 5: HexCer 4....
## 6: CL 76:4,....
## 7: PE O-32:....
## 8: SM 41:2;....
## 9: FC
## 10: PI 34:2,....
## 11: Cer 42:1....
## 12: PS 36:2,....
## 13: DG 40:5,....
## 14: PE 36:5,....
## 15: PG 32:0,....
## [1] "LNCaP_PunA10_fer13 - LNCaP_PunA10"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: TG 2.113598e-09 3.170397e-08 0.77493903 0.6531756 2.1964851 71
## 2: HexCer 2.555277e-03 1.916457e-02 0.43170770 -0.9548295 -1.8651958 4
## 3: PC O 9.258866e-03 4.629433e-02 0.38073040 -0.5792338 -1.7369066 17
## 4: PE 1.045590e-01 3.920963e-01 0.28780513 -0.3951613 -1.3465371 23
## 5: PC 1.971154e-01 4.953476e-01 0.22205605 -0.3500679 -1.2040632 25
## 6: DG 2.142857e-01 4.953476e-01 0.20429476 -0.4263566 -1.1991661 13
## 7: PE O 2.311622e-01 4.953476e-01 0.09721508 0.4650135 1.1879189 18
## 8: CL 3.612824e-01 6.647673e-01 0.07096095 0.3813376 1.0814624 27
## 9: PI 4.783163e-01 6.647673e-01 0.05896945 0.3920324 0.9945868 17
## 10: CE 5.137615e-01 6.647673e-01 0.12878871 -0.3148406 -0.9743227 19
## 11: SM 5.208877e-01 6.647673e-01 0.05605959 0.4484395 0.9810825 9
## 12: FC 5.702306e-01 6.647673e-01 0.07473852 -0.7074074 -0.9499211 1
## 13: Cer 5.761317e-01 6.647673e-01 0.11331291 -0.3939394 -0.9005333 7
## 14: PS 7.142857e-01 7.221494e-01 0.10282184 -0.2909409 -0.8093876 12
## 15: PG 7.221494e-01 7.221494e-01 0.04123761 0.3927836 0.8335889 8
## leadingEdge
## <list>
## 1: TG 48:0,....
## 2: HexCer 3....
## 3: PC O-38:....
## 4: PE 36:4,....
## 5: PC 40:8,....
## 6: DG 36:4,....
## 7: PE O-36:....
## 8: CL 72:11....
## 9: PI 36:2,....
## 10: CE 20:1,....
## 11: SM 41:2;....
## 12: FC
## 13: Cer 36:1....
## 14: PS 38:0,....
## 15: PG 36:0,....
## [1] "PC3_PunA10_fer13 - PC3_PunA10"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: PE 0.002206284 0.03309425 0.431707696 -0.5871879 -1.6785058 23
## 2: DG 0.035012436 0.12182741 0.321775918 -0.5853422 -1.5403146 13
## 3: HexCer 0.035888786 0.12182741 0.321775918 0.6891386 1.4952074 4
## 4: CE 0.041067762 0.12182741 0.224966094 -0.5119030 -1.4273836 19
## 5: SM 0.044516830 0.12182741 0.222056046 -0.5981739 -1.4504319 9
## 6: PC 0.048730964 0.12182741 0.204294757 -0.4763114 -1.3761175 25
## 7: PE O 0.086508754 0.18537590 0.151148761 -0.4804247 -1.3311464 18
## 8: PS 0.142172746 0.26657390 0.249246554 0.3513514 1.2803374 12
## 9: Cer 0.285554311 0.42943548 0.078089231 -0.5001018 -1.1420579 7
## 10: CL 0.286290323 0.42943548 0.072535186 -0.3822253 -1.1186197 27
## 11: PC O 0.674586777 0.88153340 0.033309282 -0.3216639 -0.8847136 17
## 12: FC 0.715725806 0.88153340 0.061570676 0.6407407 0.8689321 1
## 13: PG 0.763995609 0.88153340 0.030204195 -0.3418596 -0.8075474 8
## 14: TG 0.963036963 0.99070248 0.008938012 -0.2214872 -0.7032335 71
## 15: PI 0.990702479 0.99070248 0.009547607 -0.1854119 -0.5099620 17
## leadingEdge
## <list>
## 1: PE 36:1,....
## 2: DG 34:1,....
## 3: HexCer 4....
## 4: CE 22:5,....
## 5: SM 36:1;....
## 6: PC 38:4,....
## 7: PE O-36:....
## 8: PS 40:5,....
## 9: Cer 40:1....
## 10: CL 72:3,....
## 11: PC O-36:....
## 12: FC
## 13: PG 34:1,....
## 14: TG 53:1,....
## 15: PI 40:4,....
## [1] "int_LNCaP_PunA10_fer13_PunA10"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: TG 0.0009389503 0.01408425 0.47727082 -0.5454651 -1.6368469 71
## 2: CE 0.0614250614 0.46068796 0.19991523 -0.5793783 -1.4279282 19
## 3: PC O 0.2536945813 0.79827602 0.08971047 -0.4900690 -1.1771583 17
## 4: CL 0.2597402597 0.79827602 0.22496609 0.3070716 1.1096552 27
## 5: SM 0.2828418231 0.79827602 0.08835944 -0.5548198 -1.1624671 9
## 6: PS 0.4750000000 0.79827602 0.12750532 0.3466578 0.9963594 12
## 7: PE O 0.5025641026 0.79827602 0.13880511 0.3030956 0.9760878 18
## 8: HexCer 0.5318471338 0.79827602 0.10208011 0.4768318 0.9195012 4
## 9: PI 0.5421052632 0.79827602 0.13500203 0.3110236 0.9738083 17
## 10: FC 0.5431372549 0.79827602 0.07380527 0.7259259 0.9723018 1
## 11: DG 0.6308290155 0.79827602 0.04688199 -0.3939988 -0.8921602 13
## 12: Cer 0.6395027624 0.79827602 0.04919275 -0.4513560 -0.8878220 7
## 13: PE 0.6918392205 0.79827602 0.03984069 -0.3309292 -0.8465634 23
## 14: PC 0.9587878788 0.98378378 0.02350848 -0.2196950 -0.5713662 25
## 15: PG 0.9837837838 0.98378378 0.02794028 -0.2413391 -0.4931316 8
## leadingEdge
## <list>
## 1: TG 57:4,....
## 2: CE 20:5,....
## 3: PC O-38:....
## 4: CL 76:4,....
## 5: SM 40:2;....
## 6: PS 32:1,....
## 7: PE O-32:....
## 8: HexCer 4....
## 9: PI 34:0,....
## 10: FC
## 11: DG 40:5,....
## 12: Cer 42:1....
## 13: PE 36:5,....
## 14: PC 38:3,....
## 15: PG 32:0,....
## [1] "int_PC3_PunA10_fer13_PunA10"
## pathway pval padj log2err ES NES size
## <char> <num> <num> <num> <num> <num> <int>
## 1: TG 0.003052349 0.04578524 0.43170770 -0.4617136 -1.4147614 71
## 2: CE 0.077235772 0.47242084 0.15964670 -0.4911780 -1.3277904 19
## 3: SM 0.096359743 0.47242084 0.14551615 -0.5737531 -1.3282793 9
## 4: PE 0.141271443 0.47242084 0.11331291 -0.4437498 -1.2356147 23
## 5: HexCer 0.187134503 0.47242084 0.25296112 0.5692884 1.2148586 4
## 6: PE O 0.188968335 0.47242084 0.09592068 -0.4417600 -1.1828169 18
## 7: DG 0.301455301 0.64597565 0.07147863 -0.4426707 -1.1169071 13
## 8: PI 0.348002684 0.65250503 0.20350896 0.2322835 0.9729923 17
## 9: PC 0.452811245 0.75468541 0.05039643 -0.3647779 -1.0283733 25
## 10: CL 0.539539540 0.79354553 0.04224682 -0.3473464 -0.9855149 27
## 11: Cer 0.600877193 0.79354553 0.04148804 -0.4163695 -0.9126322 7
## 12: PG 0.661272923 0.79354553 0.03630185 -0.3902271 -0.8856146 8
## 13: FC 0.687739464 0.79354553 0.06103637 0.6666667 0.8888931 1
## 14: PC O 0.899897855 0.94764398 0.01683805 -0.2733929 -0.7273005 17
## 15: PS 0.947643979 0.94764398 0.01485926 -0.2528732 -0.6250262 12
## leadingEdge
## <list>
## 1: TG 53:1,....
## 2: CE 20:2,....
## 3: SM 41:2;....
## 4: PE 36:1,....
## 5: HexCer 4....
## 6: PE O-38:....
## 7: DG 34:1,....
## 8: PI 40:5,....
## 9: PC 38:4,....
## 10: CL 72:3,....
## 11: Cer 42:1....
## 12: PG 34:1,....
## 13: FC
## 14: PC O-36:....
## 15: PS 36:1,....2.10 ALL PunA
all_punA = function(cellLine){
res_list <- lapply(fit_list, function(x) test_diff(x,
paste0(cellLine,"_PunA2.5 - ",
cellLine,"_Control"),
alternative = "two.sided",
pval_adjust_method = "BH",
sort_by = NULL,
verbose = F))
res_PunA1 <- do.call(rbind,res_list)
res_PunA1$adj_pval <- p.adjust(res_PunA1$pval, method = "fdr")
res_PunA1 = res_PunA1 %>%
as_tibble() %>% dplyr::rename("lipid" = "name",
"adj.P.Val" = "adj_pval")%>%
rename_all(~paste0(.x,"_PunA2.5"))
res_list <- lapply(fit_list, function(x) test_diff(x,
paste0(cellLine,"_PunA5 - ",
cellLine,"_Control"),
alternative = "two.sided",
pval_adjust_method = "BH",
sort_by = NULL,
verbose = F))
res_PunA2 <- do.call(rbind,res_list)
res_PunA2$adj_pval <- p.adjust(res_PunA2$pval, method = "fdr")
res_PunA2 = res_PunA2 %>%
as_tibble() %>% dplyr::rename("lipid" = "name",
"adj.P.Val" = "adj_pval")%>%
rename_all(~paste0(.x,"_PunA5"))
res_list <- lapply(fit_list, function(x) test_diff(x,
paste0(cellLine,"_PunA10 - ",
cellLine,"_Control"),
alternative = "two.sided",
pval_adjust_method = "BH",
sort_by = NULL,
verbose = F))
res_PunA3 <- do.call(rbind,res_list)
res_PunA3$adj_pval <- p.adjust(res_PunA3$pval, method = "fdr")
res_PunA3 = res_PunA3 %>%
as_tibble() %>% dplyr::rename("lipid" = "name",
"adj.P.Val" = "adj_pval") %>%
rename_all(~paste0(.x,"_PunA10"))
df <- res_PunA1 %>%
inner_join(res_PunA2, by = c("lipid_PunA2.5"="lipid_PunA5")) %>%
inner_join(res_PunA3, by = c("lipid_PunA2.5"="lipid_PunA10")) %>%
dplyr::rename("lipid_name"=lipid_PunA2.5) %>%
left_join(rownames_to_column(as.data.frame(rd)),
by = c("lipid_name"="rowname")) %>%
select(c(lipid_name,Lipid_Class, contains("diff"),
contains("adj.P.Val")))
colnames(df) = sub("diff_","logFC_",colnames(df))
colnames(df) = sub("adj.P.Val_","adjpval_",colnames(df))
df_long = df %>%
pivot_longer(cols = c(contains("logFC_"),
contains("adjpval_"))) %>%
mutate(test = sub("logFC_|adjpval_","",name),
name = sub("_.*","",name))%>%
pivot_wider(names_from = name, values_from = value)
df_long$test = factor(df_long$test,
levels = c("PunA2.5","PunA5","PunA10"))
df_long$signif = df_long$adjpval<0.05
p = df_long %>% ggplot(aes(x = test, y = logFC,
group = lipid_name)) +
geom_line(color="darkgray", alpha=0.7) +
facet_wrap(~ Lipid_Class, scales = "free", ncol=3) +
geom_point(aes(color=signif), alpha=0.7) +
scale_color_manual(values = c("darkgray","red")) +
ggtitle(cellLine)
p
}
3 sessionInfo
## R version 4.3.0 (2023-04-21)
## Platform: x86_64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.6.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Brussels
## tzcode source: internal
##
## attached base packages:
## [1] parallel grid stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] fgsea_1.28.0 GGally_2.2.1
## [3] Vennerable_3.0 xtable_1.8-6
## [5] gtools_3.9.5 reshape_0.8.9
## [7] RColorBrewer_1.1-3 RBGL_1.78.0
## [9] graph_1.80.0 clusterProfiler_4.10.0
## [11] doParallel_1.0.17 iterators_1.0.14
## [13] foreach_1.5.2 missForest_1.5
## [15] lipidr_2.16.0 patchwork_1.2.0
## [17] limma_3.58.1 proDA_1.16.0
## [19] UpSetR_1.4.0 knitr_1.45
## [21] glmnet_4.1-8 Matrix_1.6-5
## [23] randomForest_4.7-1.1 caret_6.0-94
## [25] lattice_0.22-5 xlsx_0.6.5
## [27] plotly_4.10.4 viridis_0.6.5
## [29] viridisLite_0.4.2 ComplexHeatmap_2.18.0
## [31] FactoMineR_2.10 factoextra_1.0.7
## [33] readxl_1.4.3 gplots_3.1.3.1
## [35] missMDA_1.19 QFeatures_1.12.0
## [37] MultiAssayExperiment_1.28.0 pander_0.6.5
## [39] ggrepel_0.9.5 lubridate_1.9.3
## [41] forcats_1.0.0 stringr_1.5.1
## [43] purrr_1.0.2 readr_2.1.5
## [45] tidyr_1.3.1 tibble_3.2.1
## [47] tidyverse_2.0.0 SummarizedExperiment_1.32.0
## [49] Biobase_2.62.0 GenomicRanges_1.54.1
## [51] GenomeInfoDb_1.38.6 IRanges_2.36.0
## [53] S4Vectors_0.40.2 BiocGenerics_0.48.1
## [55] MatrixGenerics_1.14.0 matrixStats_1.2.0
## [57] dplyr_1.1.4 ggplot2_3.5.0
##
## loaded via a namespace (and not attached):
## [1] nnet_7.3-19 DT_0.32 Biostrings_2.70.2
## [4] TH.data_1.1-2 vctrs_0.6.5 digest_0.6.34
## [7] png_0.1-8 shape_1.4.6.1 parallelly_1.37.1
## [10] MASS_7.3-60.0.1 reshape2_1.4.4 qvalue_2.34.0
## [13] withr_3.0.0 MultiDataSet_1.30.0 ropls_1.34.0
## [16] xfun_0.42 ggfun_0.1.4 ellipsis_0.3.2
## [19] survival_3.5-8 doRNG_1.8.6 memoise_2.0.1
## [22] gmm_1.8 emmeans_1.10.0 gson_0.1.0
## [25] calibrate_1.7.7 tidytree_0.4.6 zoo_1.8-12
## [28] GlobalOptions_0.1.2 KEGGREST_1.42.0 scatterplot3d_0.3-44
## [31] httr_1.4.7 globals_0.16.2 rstudioapi_0.15.0
## [34] pan_1.9 generics_0.1.3 DOSE_3.28.2
## [37] curl_5.2.1 zlibbioc_1.48.0 ggraph_2.2.0
## [40] polyclip_1.10-6 GenomeInfoDbData_1.2.11 SparseArray_1.2.4
## [43] evaluate_0.23 S4Arrays_1.2.0 BiocFileCache_2.10.1
## [46] hms_1.1.3 colorspace_2.1-0 filelock_1.0.3
## [49] magrittr_2.0.3 ggtree_3.10.1 MsCoreUtils_1.14.1
## [52] future.apply_1.11.1 shadowtext_0.1.3 cowplot_1.1.3
## [55] class_7.3-22 pillar_1.9.0 nlme_3.1-164
## [58] caTools_1.18.2 compiler_4.3.0 stringi_1.8.3
## [61] gower_1.0.1 jomo_2.7-6 minqa_1.2.6
## [64] tmvtnorm_1.6 extraDistr_1.10.0 plyr_1.8.9
## [67] crayon_1.5.2 abind_1.4-5 gridGraphics_0.5-1
## [70] graphlayouts_1.1.0 bit_4.0.5 sandwich_3.1-0
## [73] pcaMethods_1.94.0 fastmatch_1.1-4 codetools_0.2-19
## [76] multcomp_1.4-25 recipes_1.0.10 crosstalk_1.2.1
## [79] bslib_0.6.1 GetoptLong_1.0.5 splines_4.3.0
## [82] circlize_0.4.16 Rcpp_1.0.12 dbplyr_2.4.0
## [85] HDO.db_0.99.1 cellranger_1.1.0 leaps_3.1
## [88] blob_1.2.4 utf8_1.2.4 clue_0.3-65
## [91] AnnotationFilter_1.26.0 lme4_1.1-35.1 itertools_0.1-3
## [94] fs_1.6.3 listenv_0.9.1 ggplotify_0.1.2
## [97] estimability_1.5 statmod_1.5.0 tzdb_0.4.0
## [100] tweenr_2.0.3 pkgconfig_2.0.3 tools_4.3.0
## [103] cachem_1.0.8 RSQLite_2.3.5 DBI_1.2.2
## [106] impute_1.76.0 fastmap_1.1.1 rmarkdown_2.25
## [109] scales_1.3.0 broom_1.0.5 sass_0.4.8
## [112] coda_0.19-4.1 ggstats_0.5.1 rpart_4.1.23
## [115] farver_2.1.1 tidygraph_1.3.1 scatterpie_0.2.1
## [118] yaml_2.3.8 cli_3.6.2 lifecycle_1.0.4
## [121] mvtnorm_1.2-4 lava_1.7.3 backports_1.4.1
## [124] BiocParallel_1.36.0 timechange_0.3.0 gtable_0.3.4
## [127] rjson_0.2.21 pROC_1.18.5 ape_5.7-1
## [130] jsonlite_1.8.8 mitml_0.4-5 bitops_1.0-7
## [133] multcompView_0.1-9 bit64_4.0.5 yulab.utils_0.1.4
## [136] mice_3.16.0 highr_0.10 jquerylib_0.1.4
## [139] GOSemSim_2.28.1 imputeLCMD_2.1 timeDate_4032.109
## [142] lazyeval_0.2.2 htmltools_0.5.7 rJava_1.0-11
## [145] enrichplot_1.22.0 GO.db_3.18.0 glue_1.7.0
## [148] XVector_0.42.0 RCurl_1.98-1.14 treeio_1.26.0
## [151] gridExtra_2.3 boot_1.3-30 flashClust_1.01-2
## [154] igraph_2.0.2 R6_2.5.1 labeling_0.4.3
## [157] xlsxjars_0.6.1 cluster_2.1.6 rngtools_1.5.2
## [160] aplot_0.2.2 ipred_0.9-14 nloptr_2.0.3
## [163] DelayedArray_0.28.0 tidyselect_1.2.0 ProtGenerics_1.34.0
## [166] ggforce_0.4.2 qqman_0.1.9 AnnotationDbi_1.64.1
## [169] future_1.33.1 ModelMetrics_1.2.2.2 munsell_0.5.0
## [172] KernSmooth_2.23-22 data.table_1.15.2 htmlwidgets_1.6.4
## [175] rlang_1.1.3 Cairo_1.6-2 norm_1.0-11.1
## [178] fansi_1.0.6 hardhat_1.3.1 prodlim_2023.08.28